This article discusses the application methods of distributed databases in management information systems. First, the two main types of structured and isomorphic databases are analyzed. Then, it elaborates on the application technology of distributed databases from five aspects: structural design, data transmission, security strategy, function implementation, and data synchronization maintenance, and proposes the selection of network topology mode, data segmentation strategy, and optimized transmission algorithm according to business needs. , establish security protection mechanisms, and support OLTP and OLAP processing methods. Finally, distributed databases provide essential support for management information systems by horizontally expanding storage and computing capabilities but also bring technical challenges.

The growing threat that marine pollution poses to human health and biodiversity requires innovative responses. This study aims to create autonomous, unmanned vessels for collecting marine debris to prevent the negative effects of marine litter, especially plastics and industrial waste. The study uses MATLAB Simulink for dynamic modeling, simulating how the ship interacts with different debris and maritime settings. The effectiveness of advanced control systems that combine feedforward and feedback controls is evaluated regarding debris collection and pathfinding. The methodology of this study involves the design and simulation of the vessel’s hydrodynamic behavior, control systems, and debris interaction mechanisms. A scaled-down prototype has been built, integrating elements like a double-propeller hull, Arduino-driven control mechanisms, and a debris-gathering system. The prototype’s efficiency in gathering debris and mobility is demonstrated by real-world testing conducted in controlled situations. According to the results, the integration of modern control systems has been found to greatly improve the vessel’s debris collection efficiency. How the prototype functions in different situations demonstrates its ability to serve as a scalable solution for marine pollution. The result of this study emphasizes the possibility of automated, unmanned marine debris collection vessels as an environmentally friendly way to reduce marine pollution. The effective prototype testing, modeling, and simulation bring up new avenues for research and development in this area, which benefits the preservation of marine ecosystems.

This paper presents a model that integrates a BERT encoder with a Capsule network, eliminating the traditional fully connected layer designed for downstream classification tasks in BERT in favor of a capsule layer. This capsule layer consists of three main modules: the representation module, the probability module, and the reconstruction module. It transforms the final hidden layer output of BERT into the final activation capsule probabilities to classify the text. By applying the model to sentiment analysis and text classification tasks, and comparing the test results with various BERT variants, the performance across all metrics was found to be superior. Observing the model’s handling of multiple entities and complex relationships, sentences with high ambiguity were extracted to observe the probability distribution of all capsules and compared with RNN-Capsule. It was found that the activation capsule probabilities for BERT-Capsule were significantly higher than the rest, and more pronounced than RNN-Capsule, indicating the model’s exceptional ability to process ambiguous information.

This essay explores the dynamic landscape of gastrointestinal health, highlighting the increasing incidence of conditions such as infections, ulcers, bleeding, and cancers. The crucial imperative of timely detection and diagnosis is emphasized, leading to examining capsule endoscopy and the transformative impact of magnetic sensor-based positioning techniques. From the initial development of capsule endoscopy in 2001 to the evolution of Simultaneous Magnetic Actuation and Localization (SMAL), the essay delves into advancements that enhance precision and control within the intricate gastrointestinal tract. Notwithstanding notable progress, challenges such as the necessity for miniaturization, reliance on external magnetic fields, and safety considerations are acknowledged. Future developments in navigation algorithms, wireless communication, and power management are anticipated and discussed as potential solutions. Envisioning a future where magnetic sensor-based capsule endoscopy integrates with artificial intelligence, the essay foresees personalized medicine, home-based monitoring, and global healthcare accessibility. Ultimately, the collaborative efforts needed to overcome challenges and unleash the full potential of this technology are emphasized, shaping the trajectory of gastrointestinal healthcare.

As a crucial application of modern technology, smart lighting systems are transforming our interaction with the living environment. The key lies in utilizing advanced sensor technology to maximize lighting efficiency and conserve energy. This paper focuses on designing a sensor-based lighting control system that integrates Passive Infrared and light intensity sensors for the automated management of environmental lighting. Our research has extensively explored the integrated application of PIR and light-intensity sensors. The design aims to automatically adjust indoor lighting based on environmental light conditions and the presence or absence of occupants to enhance comfort and energy efficiency. The sensor fusion algorithm was optimized through a series of experiments to improve detection accuracy and effective energy utilization. Based on the design analysis, the system can significantly enhance energy savings by reducing unnecessary lighting without compromising occupant comfort. The system’s design also considers rooms of varying sizes and layouts, demonstrating good adaptability and versatility. The significance of this research lies in providing innovative solutions for the smart lighting domain. By enhancing the intelligence and responsiveness of lighting systems in home and commercial environments, our design offers a more intuitive and energy-efficient lighting experience. It paves the way for future developments and innovations in lighting systems. This work highlights the immense potential of sensor-based systems in creating interactive and adaptive environments suitable for diverse settings such as homes, corridors, and underground garages.

Rice is a major food crop and one of the most cadmium absorbing and bulk cereal crops. OsCTF, a gene involved in the efflux of heavy metal Cd in rice, which is closely related to the amount of Cd accumulation in rice. In this study, CRISPR-dCas9 technology was used to regulation of OsCTF 3 ‘- UTR demethylation in rice to improve the amount of OsCTF gene expression, which was detected by direct detection, using a Chop-PCR method to detect the methylation level in rice, and real-time quantitative PCR to detect the level of OsCTF gene expression; at the same time,cadmium stress was detected, DNA methylation was analyzed by sulfite sequencing and McrBC-PCR, and OsCTF gene expression was detected by differential expression gene analysis. OsCTF expression and cadmium accumulation in rice after demethylation of the OsCTF 3 ‘- UTR were measured by both direct and cadmium stress assays. Then, OsCTF 3 ‘-UTR demethylation level, OsCTF gene expression and cadmium accumulation in offspring were measured by self-cross method of transgenic rice. Finally, a new rice line with low cadmium accumulation was formed.

Text sentiment analysis is a crucial aspect within the realm of natural language processing. This paper, incorporating the latest research advancements, systematically reviews and summarizes mainstream methods in text sentiment analysis. It covers a spectrum of traditional text analysis techniques, including rule-based and dictionary-based methods, as well as machine learning approaches. The paper further delves into an in-depth discussion of text sentiment analysis methods based on deep learning. This encompasses Recurrent Neural Networks and their enhanced structures, Convolutional Neural Networks and their extended techniques, along with applications involving pre-trained models and transfer learning.

This paper thoroughly investigates electromagnetic waves (EM), highlighting their fundamental characteristics, applications in electrical energy transmission, and mobile phone technology. The study begins by dissecting EM waves’ wave-particle duality and spectrum range, establishing a foundation to understand their significant role in modern technology and society. A key focus is placed on their efficiency in electrical energy transmission, especially relevant in the context of increasing energy demands and the integration of renewable resources for sustainable development. The paper also examines how electromagnetic waves have revolutionized mobile phone technology, encompassing aspects like wireless connectivity, GPS, and near-field communication, profoundly influencing global communication and information exchange. Despite these advancements, the paper acknowledges challenges like health and environmental risks associated with electromagnetic pollution. These complexities underscore the need for balanced advancement in electromagnetic wave applications and technology.

With the continuous progress of science and technology, all kinds of electrical and electronic equipment are applied to production and life. However, bad electromagnetic interference will affect the normal operation of the equipment and reduce work efficiency. This paper analyzes the situation and source of electromagnetic interference in the industrial field and introduces several common anti-interference methods. And take corresponding measures according to different types of interference. This paper introduces electromagnetic shielding, filtering, and other technologies, and the application scenarios of various anti-interference methods are analyzed. When the working environment and the nature of interference emission change, the anti-interference strategy should be adjusted accordingly. Implementing these measures will significantly improve the electromagnetic compatibility of the system, reduce the electromagnetic interference phenomenon in the industrial site, and ensure the stable and efficient work of the system. Although electromagnetic interference cannot be eliminated, summarizing and analyzing existing technologies helps to develop new technologies and provides more possibilities for anti-interference.

With the increasing development and popularity of smart wearable devices, smart bracelets have become one of the hottest smart devices today. Through a comparative analysis of existing products, this paper proposes improvement strategies in the three main directions of UI and module design, security, and handling of health issues to provide a product that is easier to use and meets the health needs of the elderly. For the direction of UI and module design, it is proposed to add the gesture recognition function and develop it in the direction of non-contact recognition based on the existing technology, and at the same time, use machine learning to achieve personalized recognition. Based on personalized module design, a GPS tracking device is installed to prevent users from being tracked, and a new network module is added to enable users to get in touch with others at a time when they encounter danger in remote areas. Improvement strategies in the security direction include data transmission, storage, and device security. The smart bracelet can use trusted computing for data encryption and storage by adding low-power core and optimizing the data transmission in three paths: local area network (LAN), public network (PN), and the cloud.Regarding health problem processing, it is proposed to achieve fall prevention through scene recognition and optimize the fall detection function through user information. In addition, this paper takes UV irradiation as an example to point out that smart bracelet devices can be added with new indicator detection technology to prevent some diseases. At the same time, a full voice interaction system can be developed to meet the social needs of the elderly.