Specific Implementation Methods Research Articles

Analysis of Rural Elderly Care Strategies: “Policy Guidance-Resource Integration” Pathway

With the development of China's social economy and the aging population, rural areas are facing increasingly challenging elderly care issues due to labor outflow and economic development lagging behind urban areas. To address this problem, this study collected a large amount of relevant literature, summarized and analyzed existing research, and explored pathways to effectively improve the quality of rural elderly care services. Through interdisciplinary theoretical analysis and practical research, the study proposed the “Policy Guidance-Resource Integration” pathway, confirmed its effectiveness, and elaborated on specific implementation methods and underlying rationale. The research findings demonstrate that the “Policy Guidance-Resource Integration” pathway can effectively address some of the current issues in rural elderly care, offering new insights and guidance for academic research and policy formulation in the field of rural elderly care.

Innovative Applications of Artificial Intelligence in Specific Domains

Artificial Intelligence (AI) is experiencing an unprecedented surge in development in today’s era, acting as a powerful wave sweeping across various industries and becoming the core force driving industry transformation. With its unique technological advantages and innovative capabilities, AI has shown remarkable potential in specific fields such as healthcare, education, and industry, opening up new and effective ways to solve long-standing practical problems and significantly enhance efficiency and quality. This paper will closely integrate the rich practical experience of Chongqing Soft River Turing AI Technology Co., Ltd. in relevant fields, and delve into the specific application scenarios, implementation methods, and actual effects of AI in these fields. Through the analysis of specific cases and data support, it reveals how AI can accurately address industry pain points, optimize business processes, improve production efficiency, and enhance service quality, as well as how it can bring extensive and positive impacts on society. We firmly believe that in-depth research and summary of these practical experiences can provide valuable references and insights for the further development of AI in various fields, promoting AI technology to better serve society and create a better future for humanity. At the same time, we also hope to showcase Chongqing Soft River Turing AI Technology Co., Ltd.’s innovative capabilities and leading position in the field of AI, to win more attention and support for the company’s development.

Research on the Application of Mathematical Models in Economic Forecasting

This study aims to explore the specific applications and implementation methods of mathematical models in economic forecasting. By analyzing classical mathematical models, modern mathematical models, and machine learning and artificial intelligence models, the study proposes strategies for optimizing economic forecasting models. The results show that mathematical models significantly improve the accuracy and efficiency of economic forecasts, providing scientific evidence for future economic decision-making. The paper also discusses the comprehensive application and optimization of models, offering new insights for the future direction of economic forecasting.

Exploration and Research on the Project-Driven Teaching Mode of Building Materials and Testing Experimental Course

The project-driven experimental course teaching model focuses on the cultivation of students’ practical and innovative abilities, with projects as the core. This study takes the course “Building Materials and Testing” as an example to explore and evaluate the effectiveness of specific implementation methods of the project-driven experimental course teaching model. Through practical case analysis, it was found that this teaching model effectively enhanced the student’s practical hands-on abilities, innovative thinking abilities, and teamwork abilities. In this teaching model, students are not just passive recipients of knowledge, but actively learn by participating in project practice. They need to apply the knowledge they have learned to solve practical problems and cooperate with team members to complete project tasks. This learning approach makes students more proactive and positive, cultivating their practical hands-on abilities and teamwork abilities. With this, the teaching of experimental courses in architectural majors can refer to and draw lessons from the project-driven experimental course teaching model. However, teachers need to pay attention to solving potential problems when implementing this teaching model to ensure optimal teaching outcomes.

An evaluation of the impact of China's green credit policy on different pathways using a CGE model.

The objective of the study was to quantitatively analyze the heterogeneous effects of different green credit implementation methods on energy, environmental, and economic systems by developing a computable general equilibrium model. The specific green credit implementation methods are divided into interest-penalty policy for energy-intensive industries andinterest preferential policy for green industries. Various approaches to implementing green credit can lead to distinct impacts on energy consumption, environmental outcomes, and economic performance. Green credit policy experiments are carried out utilizing short-, medium-, and long-term scenarios to investigate how the consequences of green credit policies evolve. The findings demonstrate that (1) implementing a penalty interest policy for energy-intensive industries can have substantial short-term environmental effects, cutting total demand for fossil energy and lowering carbon dioxide emissions significantly. As the cycle progresses, this effect will progressively fade and have a negative economic impact. (2) The interest preferential policy for the green industry has a significant promoting effect on green technology, and its energy and environmental effects will be reflected in the long term, and the effect will continue to increase, which has a positive promoting effect on the economy. (3) There are significant differences in the policy effects brought about by the different implementation methods of green credit policies. Both policies can positively affect social energy and the environment, but the effect cycles are different. When two types of interest policies are implemented in the economy, the negative economic effect of the penalty interest policy is greater than the positive effect of the preferential interest policy, which harms the macroeconomy. These conclusions will provide theoretical and practical references for the government and banks to choose a better green credit implementation path.

Research on multi-digital twin and its application in wind power forecasting

Digital twins are digital models that operate within the digital space to perform specific functions. It can positively impact the physical counterpart in terms of improved efficiency, reduced costs, increased safety, and reliability. However, most existing research has limited digital twins to a single digital twin model, and the inherent characteristics and practical applications of single digital twin models make it challenging to fully utilize their advantages in complex and dynamic environments. Therefore, this paper proposes the concept of a multi-digital twin (MDT), as well as its synergistic operation mechanism, and designs two specific implementation methods suitable for processing time series-related tasks: the single metric dynamic preference method and the multi-metrics dynamic fusion method both based on a time window. Then, the results of two experiments show that the average relative improvement of two methods in the MAE, RMSE, and R2 metrics were 7.83 %, 5.01 %, and 1.24 % in 2015, and 4.98 %, 2 %, and 0.29 % in 2017. This means that the proposed methods can improve the accuracy of wind power forecasting. The effectiveness of the MDT synergy operation mechanism is verified, providing new research ideas for digital twins to adapt to complex system changes.

Research and Practice of the Course Ideological and Political Education in the Case-Driven “Principles and Applications of Big Data Analysis” Course

Taking the case driven “Principles and Applications of Big Data Analysis” course ideological and political education as the research object, we analyze the current problems of ideological and political education in science and engineering courses in universities, and propose an overall design framework for course ideological and political education. The principles and criteria for case selection were provided, while typical cases were provided for the four ideological and political elements of science and technology: Serving the country, scientific thinking, engineering ethics, and craftsmanship spirit. The specific implementation methods of case discussions were also introduced in detail. Through a questionnaire survey, it was found that case driven curriculum ideological and political education has a positive impact on students’ learning effectiveness and ideological and political literacy. This study provides new ideas and methods for the ideological and political education of science and engineering courses in universities, and has certain significance for the practice of ideological and political education in science and engineering courses in universities.

New Trends in Digital Marketing: Analysis of Personalized Brand Communication and User Interaction Effect - Taking Xiaohongshu Platform as an Example

In the development of digital marketing, personalized brand communication, and user interaction analysis have become key components of enterprise strategy. Taking the Xiaohongshu platform as the research object, this article deeply explores how brands can use personalized communication strategies to establish emotional connections with users under new trends, thereby improving user participation and loyalty, and ultimately achieving the goal of enhancing brand image and market competitiveness. Firstly, this article analyzes the characteristics of the Xiaohongshu platform and its role in digital marketing. As a platform that integrates social networking and e-commerce, Xiaohongshu provides a unique environment for brands to attract and maintain user attention through high-quality content and precise target positioning. Next, the article discusses in detail the specific implementation methods of personalized communication strategies. This includes utilizing data analysis to understand user needs, creating content related to users’ personal experiences and interests, and enhancing user engagement through interaction and feedback mechanisms. Finally, this article evaluates the impact of these strategies on user loyalty and brand image. Research has shown that effective personalized communication can not only enhance users’ sense of brand identity but also increase their participation in brand activities, thereby gaining an advantage for the brand in a fiercely competitive market. In summary, this article provides profound insights into brand personalized communication strategies under the new trend of digital marketing and practical guidance for enterprises on how to maintain competitiveness in rapidly changing market environments.

Connotative Qualities and the Pathway of Unit-Based High School Chinese Teaching

Relying on the existing theories and adopting the dialectical analysis method, we explore the guiding significance and the specific implementation methods of the unit-based Chinese teaching mode on high school classroom from theory to practice, from the basics and go deeper. A study on the design methodology of the unit-based Chinese teaching is based on its importance for students’ development in order to provide an effective method for building a human-centered high school Chinese classroom and to look forward to the bright future of the unit-based teaching mode.

高校心理健康四轮驱动工作模式构建探究

This article proposes a study on the construction of a four-wheel drive working model for mental health in colleges and universities, aiming to improve the mental health level of college students through a comprehensive and systematic approach. This model consists of a mental health education module, a mental health service module, an organizational structure and crisis intervention module, and a module aimed at improving the connotation and professional growth of the staff working in mental health work in colleges and universities. First, it elaborates on the current goals of mental health work in colleges and universities, and then introduces the construction ideas and specific implementation methods of the four-wheel drive model, and uses the actual case of Quanzhou Marine Vocational College to demonstrate the feasibility and effect of this model. The promotion and application of this model will help improve the effectiveness and quality of mental health work in colleges and universities, and promote the overall development of college students’ physical and mental health.

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Optoelectronic materials are essential for today’s scientific and technological development, and machine learning provides new ideas and tools for their research. In this paper, we first summarize the development history of optoelectronic materials and how materials informatics drives the innovation and progress of optoelectronic materials and devices. Then, we introduce the development of machine learning and its general process in optoelectronic materials and describe the specific implementation methods. We focus on the cases of machine learning in several application scenarios of optoelectronic materials and devices, including the methods related to crystal structure, properties (defects, electronic structure) research, materials and devices optimization, material characterization, and process optimization. In summarizing the algorithms and feature representations used in different studies, it is noted that prior knowledge can improve optoelectronic materials design, research, and decision-making processes. Finally, the prospect of machine learning applications in optoelectronic materials is discussed, along with current challenges and future directions. This paper comprehensively describes the application value of machine learning in optoelectronic materials research and aims to provide reference and guidance for the continuous development of this field.

The impact of China’s energy saving and emission reduction demonstration city policy on urban green technology innovation

Urban green technology innovation (UGTI) is strongly tied to environmental regulations, which can successfully balance economic and environmental benefits. Selecting the panel data for 280 Chinese cities during 2006–2019, we take the energy saving and emission reduction (ESER) demonstration city policy as a quasi-natural experiment, then employ the difference-in-differences model to examine the effect and its mechanisms of ESER policy on UGTI. Empirical results show that the ESER policy can significantly promote UGTI, especially in the western region, the northern region, and cities with weak government environmental attention. At the same time, China’s ESER policy has a stronger promoting effect on UGTI in cities where environmental targets are more stringent. Mechanism analysis shows that the policy mainly promotes UGTI through two channels: increasing the proportion of science and technology expenditure in fiscal expenditure and upgrading the structure of the industry. In addition, we find that the development of UGTI has positive environmental effects by lowering carbon emissions and air pollution. The findings not only enrich the literature on environmental regulation policies and UGTI at the theoretical level, but also provide references for policymakers to specific implementation methods in further enforcing environmental regulation policies to improve UGTI.

AI 자동 생성작품에 대한 저작권 부여 한계와 대안적 보호방법론

With the development of generative artificial intelligence technology, AI is emerging as a complete creative subject throughout the creative process. Therefore, in this paper, the concept of artificial intelligence-generated works (AGW) with minimal human intervention among AI-related creations is clearly defined through comparison with adjacent concepts, and then comprehensively summarizes the limitations of AGW protection in the interpretation of the current copyright law, and the international trend and overseas trends are briefly checked. Second, if it is difficult to protect by granting copyrights to AGW, first, consider how to leave it as a public domain, but also review the theoretical grounds for the theory of putting public domain and specific implementation methods thereof. Furthermore, we examine the limitations and problems of the theory of public domains, such as concerns about the possibility of abuse and false registration, as well as alternative solutions, to see what problems neglecting AGW, which is difficult to distinguish from human creation, can cause. Third, as a concrete protection methodology that has been discussed as an alternative solution, we look at the unification/dualization method and the method of subsuming within the copyright normative system or enacting a special law. In either way, the protection of AGW is premised on discriminatory protection, so this paper focuses on the theory of so-called ‘thin copyright protection’ and examines the key points that need to be reviewed in case of discriminatory protection.

Interactive Design of Museum Display System in the Context of New Media

With the continuous development and popularization of Internet technology, people's lifestyles have undergone earth-shaking changes. In today's society, people's demand for information is increasing, and traditional media forms can no longer meet the public's demand for information. For example, more and more users will access museum information and exhibit it through mobile phones or computers. At the same time, some merchants will take advantage of this convenience to conduct online exhibitions to attract more tourists to visit cultural relics and monuments or to post content about the collection on the Internet to spread cultural and educational purposes. This paper mainly focuses on the characteristics of new media, combined with the analysis of the current status of interaction design. The interaction design idea of a museum exhibit display system in the context of new media is proposed. Through the in-depth study of the characteristics of new media, combined with the functional requirements of the museum exhibit display system and the characteristics of the user group, the design principles and specific implementation methods of the interface of the museum exhibit display system are proposed.

An Accurate Detection Model of Takifugu rubripes Using an Improved YOLO-V7 Network

In aquaculture, the accurate recognition of fish underwater has outstanding academic value and economic benefits for scientifically guiding aquaculture production, which assists in the analysis of aquaculture programs and studies of fish behavior. However, the underwater environment is complex and affected by lighting, water quality, and the mutual obscuration of fish bodies. Therefore, underwater fish images are not very clear, which restricts the recognition accuracy of underwater targets. This paper proposes an improved YOLO-V7 model for the identification of Takifugu rubripes. Its specific implementation methods are as follows: (1) The feature extraction capability of the original network is improved by adding a sizeable convolutional kernel model into the backbone network. (2) Through ameliorating the original detection head, the information flow forms a cascade effect to effectively solve the multi-scale problems and inadequate information extraction of small targets. (3) Finally, this paper appropriately prunes the network to reduce the total computation of the model; meanwhile, it ensures the precision of the detection. The experimental results show that the detection accuracy of the improved YOLO-V7 model is better than that of the original. The average precision improved from 87.79% to 92.86% (when the intersection over union was 0.5), with an increase of 5.07%. Additionally, the amount of computation was reduced by approximately 35%. This shows that the detection precision of the proposed network model was higher than that for the original model, which can provide a reference for the intelligent aquaculture of fishes.

Diseased Fish Detection in the Underwater Environment Using an Improved YOLOV5 Network for Intensive Aquaculture

In intensive aquaculture, the real-time detection and monitoring of common infectious disease is an important basis for scientific fish epidemic prevention strategies that can effectively reduce fish mortality and economic loss. However, low-quality underwater images and low-identification targets present great challenges to diseased fish detection. To overcome these challenges, this paper proposes a diseased fish detection model, using an improved YOLOV5 network for aquaculture (DFYOLO). The specific implementation methods are as follows: (1) the C3 structure is used instead of the CSPNet structure of the YOLOV5 model to facilitate the industrial deployment of the algorithm; (2) all the 3 × 3 convolutional kernels in the backbone network are replaced by a convolutional kernel group consisting of parallel 3 × 3, 1 × 3 and 3 × 1 convolutional kernels; and (3) the convolutional block attention module is added to the YOLOV5 algorithm. Experimental results in a fishing ground showed that the DFYOLO is better than that of the original YOLOV5 network, and the average precision was improved from 94.52% to 99.38% (when the intersection over union is 0.5), for an increase of 4.86%. Therefore, the DFYOLO network can effectively detect diseased fish and is applicable in intensive aquaculture.

Research on Statistics Curriculum Reform under the Influence of Public Security Big Data Technology

In the use of big data, public security mainly focuses on the key processing of data. Although it is structured, it needs to be used in many aspects. Data processing results and analysis results, but through data processing, can effectively think about the specific implementation methods. Public security data science is becoming a new public security discipline and police technical field. Under the influence of public security big data technology, there are various problems in the development of statistics education and teaching and the design of teaching courses. On the premise of combining the design of statistics-related courses and the development of teaching, this paper makes an in-depth study on the reform of statistics courses under the influence of public security big data technology, and puts forward the reform path of statistics courses. In order to improve the teaching quality of statistics course and cultivate professionals who are more in line with the development of the times.

Humanistic quality teaching reform of material science

How to play the role of humanistic quality education in Material major courses? This paper discusses the design ideas and specific implementation methods of integrating Curriculum humanistic quality into the teaching of material science from three aspects: curriculum knowledge objectives and ability objective, strategies of curriculum teaching methods, course expected learning results. After humanistic quality teaching reform of the basic course of material science, professional knowledge points and ideological and political elements are integrated, and the teaching platform, students' real feelings and the power of humanistic quality elements are coordinated.

Fish school feeding behavior quantification using acoustic signal and improved Swin Transformer

In aquaculture, the real-time quantification of fish feeding behavior is an important basis for feeding decisions. The acoustics produced by fish chewing feed and activities during feeding can be used to quantify feeding behavior. This study proposes an Audio Spectrum Swin Transformer (ASST) model based on an acoustic signal and attention mechanism that can divide the feeding intensity of fish into four grades: strong, medium, weak, and none. The specific implementation methods are: (1) A sliding window was applied to clip the audio and the acoustic. Signals are transformed into spectrograms. (2) The perceptual domain of fish feeding acoustic spectrum recognition task was gradually expanded by adopting the Swin Transformer and utilizing its hierarchical structure. (3) The model's performance for small data sets was further improved by adding SPT, LSA, and enhanced residual connections. (4) A predictive optimization module was designed to correct the feeding strategy according to four feeding levels. The final experimental results demonstrate that the accuracy of the improved ASST network for fish feeding behavior quantification reached 96.16%. It can effectively identify-four fish feeding intensities, allowing on-demand feeding and providing a basis for developing intelligent feeding machines.

Weed detection in paddy field using an improved RetinaNet network

Weeds are one of the main hazards affecting the yield and quality of rice. In farmland ecosystem, weeds compete with rice for resources such as light, water, soil and space, and also cause plant disease by providing habitats for pests, resulting in a decline in yield of rice. Spraying herbicides is used most frequently due to their lower cost than manual weeding and they can effectively control a varieties of weeds. Precise spraying application may utilise intelligent technology to precisely limit the weed growth at specific locations and control the consumption of herbicides, which not only has lower cost and risk for the environment but also increases the economic benefits of agricultural products. The target of this work is to use deep learning model to perform weed detection in rice crop images and to achieve accurate real-time detection and low machine cost resulting in widely used in practice. For this purpose, a dataset of rice and weeds for object detection is established by means of on-site shooting and crawling of images on the web, which contains rice and eight categories of weeds. However, the overlap between crops and weeds poses a great challenge to weed detection. To overcome this challenge, this paper proposed a model named WeedDet based on RetinaNet, which improves the feature extraction ability of the backbone, feature pyramid network and detection head to deal with the complex information in the image, respectively. And the structure of feature pyramid and detection head are lightened to improve speed of detection. The specific implementation methods are as follows: Firstly, we propose Det-ResNet to reduce the loss of detailed information and improve the feature extraction ability of detailed textures by modifying the initial convolution structure of ResNet. In addition, the improved feature pyramid network fuses the features extracted by Det-ResNet with higher efficiency without loss of accuracy. Secondly, we propose Efficient Retina Head (ERetina-Head) with thin feature maps (small channel number feature maps) and large separable convolution, which not only saves memory and computation, but also pooling more powerful feature maps during training and inference. Finally, we combine SmoothL1 loss and GIoU loss to calculate the regression loss. In addition, Varifocal loss is used to unify the classification and quality evaluation branches in training. Our network achieves a high mAP of 94.1% and the frame rate of 24.3 fps, which is 5.5% mAP and 5.6 fps higher than the baseline RetinaNet. Experiments show that fps of WeedDet (24.3) is second only to YOLOv3 (26.3) while the mAP is 9.9% (0.941 vs 0.842) higher than YOLOv3, and the mAP is also higher than other models, verifying the effectiveness and efficiency of the model.