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In order to realize the deep integration of Internet and education, and realize the efficient allocation and comprehensive coverage of educational resources, the remote language learning platform based on human-computer interaction technology is proposed. The overall architecture of the system is designed with the help of PaaS and IaaS, and the hardware and software of the system are designed according to the overall architecture. In the hardware design, Hibernate framework and SpringJDBC are used to realize the interaction between business logic layer and data layer, and the interaction of language learning function and practice function. In addition, the remote wireless communication module is designed to complete the rapid communication of the system. The learning analysis model is constructed in the software design to determine the actual online learning needs of college students, and the collaborative filtering algorithm is used to recommend online interactive learning information data, so as to realize the function of long-distance language online interactive learning system in colleges and universities. The experimental results show that with the increase of the number of users, the page jump time of the three functions such as sending information, submitting homework and asking for leave are all below 0.04 s, especially the page jump time of the function of sending information always keeps the lowest. The login or registration function takes a long time to go back to the page. The maximum value is about 0.07 s. The results show that the system can still show excellent operation ability for large-scale users, and the sending information rate is fast, and the remote online interaction effect has obvious advantages. [ABSTRACT FROM AUTHOR]

Lung cancer is a chronic disease that leads to the most common deaths worldwide because lung cancer is not detected during early detection and classification. Therefore, several research works are being carried out based on early-stage lung cancer prediction using artificial intelligence (AI) technology. With the advancement of machine learning (ML) and deep learning (DL) techniques, several techniques have been developed for the early diagnosis of lung cancer. This survey paper considers the numerous research papers developed to predict lung cancer. The survey paper focused on predicting lung cancer in the following ways: a detailed survey on pre-processing and segmentation techniques used in lung cancer prediction, non-small cell lung cancer (NSCLC) prediction techniques ML, and DL approaches utilized for lung cancer prediction. A detailed survey on the use of various datasets in lung cancer prediction is included in this paper. The application and challenges of the lung cancer prediction model are also explored in this survey paper through the various existing methods. This survey is used to analyze the various limitations of the lung cancer model. The survey analysis is helpful in developing new lung cancer prediction techniques and solving the existing lung cancer model issues. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of the relationship between the correctness of the event diagram of distributed computing from the standpoint of communication between local processes of this computing and the existence of a logical clock for that diagram. The problem is analyzed using The Coq Proof Assistant software without assuming the fairness of the principle of excluded middle. That is, the obtained results are correct from the point of view of constructive logic, which is essential for computer science. It has been formally proved that the existence of a logical clock for distributed computing ensures the irreflexivity of the causal relation related to that computing. The conjecture about the fairness of the converse statement has been claimed. [ABSTRACT FROM AUTHOR]

Purpose: A proper understanding of malware characteristics is necessary to protect massive data generated because of the advances in Internet of Things (IoT), big data and the cloud. Because of the encryption techniques used by the attackers, network security experts struggle to develop an efficient malware detection technique. Though few machine learning-based techniques are used by researchers for malware detection, large amounts of data must be processed and detection accuracy needs to be improved for efficient malware detection. Deep learning-based methods have gained significant momentum in recent years for the accurate detection of malware. The purpose of this paper is to create an efficient malware detection system for the IoT using Siamese deep neural networks. Design/methodology/approach: In this work, a novel Siamese deep neural network system with an embedding vector is proposed. Siamese systems have generated significant interest because of their capacity to pick up a significant portion of the input. The proposed method is efficient in malware detection in the IoT because it learns from a few records to improve forecasts. The goal is to determine the evolution of malware similarity in emerging domains of technology. Findings: The cloud platform is used to perform experiments on the Malimg data set. ResNet50 was pretrained as a component of the subsystem that established embedding. Each system reviews a set of input documents to determine whether they belong to the same family. The results of the experiments show that the proposed method outperforms existing techniques in terms of accuracy and efficiency. Originality/value: The proposed work generates an embedding for each input. Each system examined a collection of data files to determine whether they belonged to the same family. Cosine proximity is also used to estimate the vector similarity in a high-dimensional area. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Landslides pose a constant threat to the Himalayan regions, causing severe harm to people and property, and disastrous consequences demand novel ways of early prediction and prevention. The study gives a comprehensive insight into real-time landslide prediction using data from several sensors. The study investigates the applicability of advanced machine learning algorithms such as Multiple Linear Regression (MLR), XGBoost (XGB), Random Forest (RF), and a hybrid MLR-LSTM model in predicting landslide disasters. Further, the performance of these models was evaluated by utilizing the evaluation metrics such as MSE, MAE, and RMSE. However, the hybrid MLR-LSTM model excels with MSE = 0.014, MAE = 0.140, and RMSE = 0.120. Additionally, the study demonstrates the hybrid model's practical application by demonstrating the real-time alert generation method. [ABSTRACT FROM AUTHOR]

Digital intelligent technology and equipment have been widely applied in the diversified scenarios of agricultural production in farmland. This study aims to introduce the overall architecture and technical implementation into modern digital intelligence, according to the current high-standard farmland. A summary was given on the concept, principles, and construction of digital intelligent farmland. The current status was also analyzed to prospect the trend of digital intelligent farmland in China. The smart farmland technology demonstration base greatly contributed to promoting the construction of high-standard farmland. Firstly, there were the actual production needs of agricultural crops in the intelligent technology demonstration base of Qihe County's digital intelligent ultrahigh-yield planting system science and technology college of China Agricultural University. Various environmental, soil, pest and disease, water, and fertilizer information sensors were also equipped with intelligent technology to support mechanized equipment. A digital system of application technology was integrated for the wheat and maize smart farmland, such as big data, the Internet of Things, cloud computing, and artificial intelligence. The digital intelligent ultrahigh-yield was achieved in the real-time and accurate acquisition of long-term information data on the wheat and corn through field environment stations and multispectral video, according to the actual production needs. There was accurate correlation data of multi-dimensional production, such as farmland vegetation, crop growth status, environmental indicators, and trends. The collection was obtained in the production information, scientific planting, unmanned mechanized operation, intelligent management, and full process visualization traceability of wheat and maize crops in digital intelligent ultrahigh-yield planting farmland. The technological equipment and systems were applied, including farmland agricultural information acquisition and monitoring, water and fertilizer integrated micro spray hose irrigation, intelligent pest and disease management, intelligent operation key equipment and technology, planting management, and traceability. An intelligent monitoring, control, and decision-making cloud platform was established for the total factor production of human-machine crops. Production management depended mainly on the international standard, intelligent, scientific, and green wheat and maize rotation planting modes, and technical equipment. An organic integration was also established for crop information, agricultural technology, and agricultural machinery for crop production, planting, and management decision-making. Grain production capacity was improved through a digital super high-yield farmland model. Full traceability aims to achieve high-quality agricultural materials, high yields efficient and safe production. The digital intelligent farming production and management model was implemented in the traditional farmland. Labor costs were reduced by about 50%, pesticide usage by about 30%, fertilizer savings by 15%-25%, and water savings by 50% in conventional irrigation, and 20%-30% in drip irrigation. There was a yield increase of about 15%-25% per unit area and an efficiency increase of about 25%. Crop information acquisition was realized for the timely management and maintenance at the high mechanization level in farmland. The informatization, mechanization, and intelligent operation of farmland crop production were basically achieved in the standardized, digitized, and traceable management of the entire process of wheat and maize crop production. The digital intelligent ultrahigh-yield can provide the scientific decision-making and theoretical mode of super high-yield planting for crop production management in farmland. A leading demonstration role was obtained in the construction and promotion of highstandard farmland in China. The significant progress can also provide a strong reference to greatly assist the rapid development of digitalization levels in farmland. [ABSTRACT FROM AUTHOR]

Monitoring natural phenomena using a variety of methods to predict disasters is a trend that is growing over time. However, there is a great disunity among methods and means of data analysis, formats and interfaces of storing and providing data, and software and information systems for data processing. As part of a large project to create a planetary observatory that combines data from spatially distributed geosphere monitoring systems, the efforts of leading institutes of the Russian Academy of Sciences are also aimed at creating an information and computing ecosystem to unite researchers processing and analyzing the data obtained. This article provides a brief overview of the current state of publications on information ecosystems in various applied fields, and it also proposes a concept for an ecosystem on a multiagent basis with unique technical features. The concept of the ecosystem includes the following: the ability to function in a heterogeneous environment on federal principles, the parallelization of data processing between agents using Petri nets as a mechanism ensuring the correct execution of data processing scenarios, the concept of georeferenced alarm events requiring ecosystem reactions and possible notification of responsible persons, and multilevel information protection allowing data owners to control access at each stage of information processing. [ABSTRACT FROM AUTHOR]

The paper presents a mathematical model integrated into the computational core of a decision support system (DSS) for the selection of cloud platforms under conditions of fuzzy information. The model facilitates the development of rational strategies for players’ interaction with cloud platforms, allowing to balance investment, security and quality of services provided. This study proposes a methodology for determining optimal strategies in cloud platform selection using a differential quality game under fuzzy conditions, taking into account their preferences and opportunities to use technologies related to the analysis of user activities, i.e., digital footprints. The performed computational experiments confirmed the effectiveness of the model and allowed us to visualize the results of the interaction based on synthetic data. The results of the study may be useful to analysts dealing with cloud solution selection problems under uncertainty and cybersecurity needs. The relevance of the study is due to the global trend of business processes moving to the cloud and the increasing requirements for data security in the digitalization of the economy, which makes the choice of an optimal cloud platform strategically significant. Employing a differential quality game with fuzzy information, this study introduces a reliable DSS that incorporates cost, security and QoS to select the most appropriate cloud platform. The computational experiments validate the model in reproducing plausible interactions yielding relevant insights for decision-makers.

Excavators are increasingly used across various industries, especially in dangerous environments such as mines, earthquake relief sites, and more. In these hazardous settings, the personal safety of excavator operators cannot be fully guaranteed. However, the implementation of remote control for excavators can effectively mitigate the risks associated with human operation on-site, thereby enhancing workers’ safety and operational efficiency. The excavator remote control system presented in this paper offers significant improvements compared to traditional excavator control systems, including (1) the realization of unlimited-distance remote control through the intervention of an IoT cloud server and (2) the accomplishment of semi-automatic and autonomous excavator operation via a binocular vision system. Experimental results demonstrate that the cloud-based excavator control system achieves remote control with low latency and high reliability, enabling functions such as walking, steering, and the extension of boom, stick, and bucket.

The work of a network that implements algorithms for IT diagnostics of neurological diseases based on the Internet of Things technology has been developed and modeled. The network includes a smartphone, a platform, a neural network, and applications. First, the voices of sick patients are entered from the smartphone to train the neural network, and then the examined patients for IT diagnostics. Data is transferred between the smartphone and the platform (ThingSpeak) via the MQTT protocol. The smartphone’s mobile application extracts the voice functions of the examined patients and records them on the Internet of Things network platform. Recognition is performed using the trained neural network. The structure and algorithm of the ThingSpeak platform are presented. IT diagnostics data are displayed in the application on the smartphone. The patient data used in the study are taken from the ADReSS 2020 Challenge program, which contains speech data of patients with Alzheimer’s disease and healthy people.

Objective To advance the modernization of rail transit control systems and enhance their safety, reliability and operational efficiency, a cloud-based control system architecture for rail transit is designed. The aim is to effectively integrate and deploy both the safety-critical and non-safety-critical functions of rail transit system onto a cloud platform. Method A cross-regional distributed control is realized by connecting target controllers to cloud platform servers via optical fiber. Characteristics of the rail transit cloud control system are analyzed, safety requirements of the system are outlined across six aspects: time management, space management, shared resource management, operation mode management, degradation mode management and safety monitoring. Result & Conclusion The virtualization layers of the cloud platform must be designed to ensure spatial isolation, temporal isolation, fault isolation, as well as management, predictability, security, and confidentiality. The platform scheduling mechanism includes both time-triggered and event-driven approaches, each impacting the platform temporal constraints differently. The hierarchical scheduling of the cloud platform involves four resource-sharing protocols, which must be selected based on different tasks.

Objective With the rapid development and expansion of urban rail transit, the problem of noise pollution is increasingly prominent due to the continuous extension of train operation lifespan. This not only causes severe harm to passenger riding experience and health, but also directly affects the brand image and service quality of rail transit enterprises. Therefore, it is necessary to collect and monitor noise levels during train operation to implement targeted noise control measures, maximizing the effectiveness of noise mitigation efforts. Method With a combination of hardware and software resources such as noise sensors, STM32 microcontrollers, 4G transmission modules, Keil μVision5, Python, and OneNET cloud platform, an online noise monitoring system based on IoT (Internet of Things) is designed and developed specifically for urban rail transit trains. Result & Conclusion The system is successfully tested and validated on Kunming Metro Line 4, achieving real-time monitoring of train interior noise with advantages such as short sampling cycles, high data accuracy, and low data loss and latency rates. The system can issue timely alerts when noise levels exceed national standards, effectively meeting the design objectives. Additionally, the system can be applied to other environmental noise monitoring scenarios, providing reference for environmental noise treatment, enhancing the quality of life for residents, and promoting sustainable urban development.

Based on the spring, spring MVC and MyBatis structures of the cloud platform SSM framework, an information management platform for power grid material supply chain is built. The data layer uses a variety of sensors to collect power grid material supply chain information, and the information is fed back to the data storage layer after being integrated by the logical reorganization function of the persistence layer. The data storage layer uses the multi-sensor supply chain information fusion method based on paste progress to fuse the information and store it in the database. The business logic layer calls the information in the database and uses the improved k-means clustering algorithm to detect the abnormal supply chain data information. After calculation and data control by the control layer, the data management results are displayed through the presentation layer. The experimental results show that the absolute error of data fusion is very low. It can effectively cluster data information and distinguish outlier anomaly information at the same time, and the effect of information management is good. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Method] A cross-regional distributed control is realized by connecting target controllers to cloud platform servers via opti- cal fiber. Characteristics of the rail transit cloud control system are analyzed, safety requirements of the system are outlined across six aspects: time management, space management, shared resource management, operation mode management, degradation mode management and safety monitoring. [Result & Conclusion] The virtualization layers of the cloud platform must be designed to ensure spatial isolation, temporal isola- tion, fault isolation, as well as management, predictability, security, and confidentiality. The platform scheduling mecha- nism includes both time-triggered and event-driven approaches, each impacting the platform temporal constraints differently. The hierarchical scheduling of the cloud platform involves four resource-sharing protocols, which must be selected based on different tasks. [ABSTRACT FROM AUTHOR]

[Objective] With the rapid development and expansion of urban rail transit, the problem of noise pollution is increasingly prominent due to the continuous extension of train operation lifespan. This not only causes severe harm to passenger riding experience and health, but also directly affects the brand image and service quality of rail transit enterprises. Therefore, it is necessary to collect and monitor noise levels during train operation to implement targeted noise control measures, maximizing the effectiveness of noise mitigation efforts. [Method] With a combination of hardware and software resources such as noise sensors, STM32 microcontrollers, 4G transmission modules, Keil µVision5, Python, and One NET cloud platform, an online noise monitoring system based on IoT (Internet of Things) is designed and developed specifically for urban rail transit trains. [Result & Conclusion] The system is successfully tested and validated on Kunming Metro Line 4, achieving real-time monitoring of train interior noise with advantages such as short sampling cycles, high data accuracy, and low data loss and latency rates. The system can issue timely alerts when noise levels exceed national standards, effectively meeting the design objectives. Additionally, the system can be applied to other environmental noise monitoring scenarios, providing reference for environmental noise treatment, enhancing the quality of life for residents, and promoting sustainable urban development. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Sciences Institute is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Human decomposition studies aim to understand the various factors influencing human decay to assess the deceased and develop postmortem interval (PMI) estimation methods. These types of studies are typically conducted through physical experiments examining the deceased; however, big data systems have the potential to transform how large‐scale forensic anthropology research questions can be addressed with curated images of donors with known demographic, climatic, and postmortem historical data. This study introduces ICPUTRD (Image Cloud Platform for Use in Tagging and Research on Decomposition), a web‐based software system, which enables forensic scientists to easily access, enhance (or curate), and analyze very large photographic collections documenting the longitudinal process of human decomposition. ICPUTRD, a JavaScript‐based application, was designed and built through a combination of the Waterfall and Agile software development life‐cycle methods and provides an image search and tagging features with a predefined nomenclature of forensic‐related keywords. To evaluate the system, a user study was conducted, involving 27 participants who completed pre‐ and post‐study surveys and three research tasks. Analysis of the study results confirmed the feasibility and practicality of ICPUTRD to facilitate aspects of forensic research and casework involving large collections of digital photographs of human decomposition. It was observed that the nomenclature lacked certain law enforcement keywords, so future work will focus on expanding it to ensure ICPUTRD is suited for all its intended users. [ABSTRACT FROM AUTHOR]

As the number of cars increases annually, driving behavior analysis has become a direction worthy of in-depth research in the field of public transportation. Traditional vehicle brakes generate torque during non-braking high-speed rotation, which has a negative impact on transmission efficiency. In this paper, the sparrow search algorithm was combined with the backpropagation neural network algorithm in the context of collecting and analysing multi-source data during vehicle driving, and the backpropagation neural network algorithm was improved by using the sparrow search algorithm’s global search ability. A driving behavior recognition model was built in order to accurately distinguish various driving behaviors and evaluate their impact on fuel consumption and driving safety. The experimental outcomes showed that the vehicle driving behavior recognition method based on cloud computing and the backpropagation neural network algorithm combined with the sparrow search algorithm featured a high driving behavior recognition efficiency, and the research method featured a small error in the identification of vehicle driving behavior. The prior accuracy of the driving behavior-based fuel consumption evaluation model was as high as 89.216%, and the regression fit for the employed model after training reached around 98%. To sum up, the obtained results not only allow the implementation of new ideas and methods for the intelligent vehicle driving and traffic safety management, but they also provide an important theoretical support and practical guidance for the design and optimization of intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article compares the efficiency of microservices written in Java and C#, using Spring Cloud for Java and Consul for C#. The measurements were carried out in three stages, such as basic service implementation, application of design patterns, and deployment on the Microsoft Azure cloud platform. The results illustrate that Java microservices are generally more efficient, although that difference diminishes on the cloud platform where .Net shows better optimization with Microsoft Azure.

AbstractPreviously, we described a Team-Based Ubiquitous Learning Model on Cloud Platform Model (PITTA Model). In this paper, we aimed to demonstrate the value of this model by compare learning achievement and creative problem-solving ability using the PITTA model of learning, compare between experimental group and control group by The Randomized Posttest-Only Control Group Design. The sample group comprised students from Bunditpatanasilpa Institute of Fine Arts who were enrolled in the Innovation Information Technology and Communication in Education course during the second semester of Feb-Mar 2023. A total of 50 students from two classrooms were selected using cluster random sampling. The students were divided into two groups: one consisting of 25 students who participated in PITTA model learning activities, and another group of 25 students who engaged in conventional learning activities. The research employed two main instruments: the learning achievement test and the creative problem-solving ability assessment test. Statistical analyses included the use of measures such as mean (x¯) and standard deviation (SD), as well as One-way MANOVA. The research findings revealed that the group of students utilizing the PITTA Model in their learning activities exhibited higher levels of learning achievement compared to the group engaged in regular learning activities, with statistical significance observed at the .05 level. Furthermore, the students who participated in learning activities based on the PITTA Model demonstrated superior creative problem-solving abilities compared to those involved in conventional learning activities, again reaching a statistical significance level of 0.05.

The proposal of student teaching information management conforms to the requirements of the times, which makes cloud platform technology and internet information processing particularly important. In order to keep up with the needs of the public, education and teaching management needs in-depth research. The analysis of student teaching information management can improve the monitoring of students' learning ability and physical and mental health from multiple aspects, contributing to providing a good student talent environment. This article introduced the design of a student teaching information management cloud platform based on mobile internet. This platform utilized cloud computing technology and mobile internet technology to achieve online management and sharing of student teaching information. The platform has functions such as user management, course management, grade management, and online communication, making it easy to manage and analyze students' teaching information, improving the quality and efficiency of school teaching. This article applied experimental and comparative methods to analyze the performance and functionality of the teaching information management system. The experimental data showed that the accuracy of the score query function in the system ranked first, reaching 99.92%, and the availability was 99.54%. [ABSTRACT FROM AUTHOR]

This paper presents a novel, sensor-enabled centralised monitoring system for fault detection in streetlights. Unlike traditional methods that depend on citizen reports or manual inspections, this system utilizes Internet of Things (IoT) technology to identify faults automatically and efficiently. The system comprises monitoring devices equipped with sensors and transceivers attached directly to streetlights. These devices collect data on light source functionality and potential wiring faults. This data is then wirelessly transmitted to a central transmitter, which relays it to a cloud platform. A machine learning model, trained with an accuracy of 98.8%, analyzes the data and predicts the streetlight's operational status. The predicted status and detailed fault information are displayed on a dedicated webpage, allowing stakeholders to monitor remotely. This eliminates manual searches, expedites repairs due to precise fault knowledge, and empowers linemen for faster resolution. Overall, this cost-effective system significantly improves streetlight maintenance efficiency, enhancing public safety and contributing to a well-lit urban environment. [ABSTRACT FROM AUTHOR]

The mining and metallurgical industry is a fundamental sector of China's industry, consisting of various subdivisions such as mining, mineral processing, smelting, etc. Mining and metallurgical equipment are characterized by harsh operating conditions, complex operation and maintenance, high energy consumption, significant pollution, substantial loss, and a high accident rate, resulting in an urgent need to leverage new infrastructure like 5G and the Industrial Internet to provide cloud-based digital and intelligent services for the "deaf, dumb, and dull" equipment in this industry. Therefore, constructing an industrial internet platform for cloud-based mining and metallurgical equipment is one of the crucial means to achieve resource aggregation and optimal allocation, enhance equipment efficiency and safety levels, and is also an inevitable choice for realizing the industry's digital transformation and upgrading. Based on the background and current situation of the construction of the cloud industrial Internet platform on equipment and the needs of the mining and metallurgy industry, the cloud industry Internet platform on mining and metallurgy industry equipment is studied. The construction contents of the device layer, edge layer, IaaS layer, PaaS layer, application layer, and security protection system are provided, respectively. Through constructing the three major functional systems: network, platform, and security, key industrial equipment assets, production and manufacturing systems, management systems, and resource applications have been fully integrated. The specific construction steps and effects of the platform are analyzed through a practical case of a mining and metallurgical enterprise. The proposed construction scheme can meet the needs of constructing a cloud industrial Internet platform for equipment in the mining and metallurgy industry and solve the common problems of cloud industrial equipment in the industry, so as to provide a theoretical and practical reference for mining and metallurgy enterprises to carry out similar platform construction, support fine equipment management, reduce equipment operation and maintenance costs and accident rates, promote the public service ecological construction of cloud industrial equipment solutions, and help the collaborative development of upstream and downstream of the industrial chain. [ABSTRACT FROM AUTHOR]

Background and objectives: Clinical data are essential for developing cloud platforms for intelligent diagnosis and treatment decision of diseases. However, cloud platforms for data sharing and exchange with clinicians are poorly suited. We aim to establish Eyecare-cloud, a platform which provide a novel method for clinical data and medical image sharing, to provide a convenient tool for clinicians. Methods: In this study, we displayed the main functions of Eyecare-cloud that we established. Based on clinical data from the cloud platform, we analyzed the incidence trend of the most common infantile retinal diseases, such as retinopathy of prematurity (ROP), over the past 20 years, as well as the associated risk factors for ROP occurrence. Statistical analyses were performed using GraphPad Prism (V.8.0) and SPSS software (V.26.0). Results: The Eyecare-cloud offers numerous advantages, including systematic archiving of patient information, one-click export data, simplifying data collection and management, eliminating the need for manual input of clinical information, reducing clinical data migration time, and lowering data management costs significantly. A total of 22,913 premature infants from Eyecare-cloud were included in the data analysis. Based on 20 years of premature infant screening data analysis, we found that the ROP incidence began to slowly decline starting in 2003 but showed a gradual increase trend again in 2016. The incidence of severe ROP remained relatively stable at a low level since 2010. The number of premature infants increased steadily before 2016 but decreased since then. ROP occurrence was significantly associated with male sex, lower gestational age, and lower birth weight (P < 0.001). Conclusion: Eyecare-cloud provides clinicians and researchers with convenient tools for big data analysis, which helps alleviate clinical workloads and integrate research data. This cloud platform supports the principles of predictive, preventive, and personalized medicine (PPPM/3PM), empowering clinicians and researchers to deliver more precise, proactive, and patient-centered eye care. [ABSTRACT FROM AUTHOR]

A large-scale street lights send data to the cloud platform, causing load imbalance among nodes of the server in high concurrency, resulting in communication failure. In view of this problem, based on microservices, this study proposes a dynamic weight load balancing algorithm based on the smart street lamp cloud platform. The algorithm calculates the respective weight coefficients and the initial weights of each node according to the hardware performance of each server during initialization, and dynamically adjusts the server weights according to the idle rate of CPU(Central Processing Unit) and network bandwidth during the request process to realize load optimization. By setting the minimum threshold and comparing it with the calculated remaining load rate, the weight of the server that reaches the upper limit of the load is set to 0 to prevent the server from being overloaded. The test results show that the proposed algorithm has a better load balancing effect than the minimum number of connections algorithm and the smooth weighted round robin algorithm in the experimental environment. Compared with the dynamic weight algorithm, the average response time and the actual number of concurrent connections of the proposed method are also improved. [ABSTRACT FROM AUTHOR]

This study designs an automatic monitoring and alarm system for integrated meteorological observation based on the cloud platform to improve the automatic monitoring ability for integrated meteorological observation and the operation and maintenance management level of meteorological departments. A meteorological data collector was used to collect comprehensive meteorological observation data, transmit the collected comprehensive meteorological observation data to the main control processing module, preprocess the comprehensive meteorological observation data, and finally transmit the pre-processed comprehensive meteorological observation data to the cloud service module through the data transmission module. In the cloud service module, cloud computing and an improved K-means algorithm were used to analyze the comprehensive meteorological observation data, mine abnormal comprehensive meteorological observation data, and send alarm information to the application module. Users can view the comprehensive meteorological observation data in the cloud server on the client in real-time, thus realizing automatic monitoring and alarm for comprehensive meteorological observation. Results show that the average transmission rate of various meteorological comprehensive observation data transmitted by the system is as high as 99.48%, thereby achieving clustering of abnormal meteorological comprehensive observation data. Upon detecting abnormal meteorological data, the system sends alarm information to the mobile phone client to complete the monitoring of abnormal meteorological information. [ABSTRACT FROM AUTHOR]

Our study investigates into the sericulture farming system, intending to improve both silk quality and production efficiency and decrease human intervention with worms. The primary elements influencing silkworms-temperature, humidity, and air quality-are meticulously regulated using an Arduino microcontroller and the Internet of Things (IoT). Data from on-field sensors is easily sent to a cloud platform, enabling farmers to monitor the fields remotely at any time with an internet connection. Our major goal is to attain top-tier silk quality with little human interaction, providing an inexpensive and effective answer to the sericulture sector. [ABSTRACT FROM AUTHOR]

In order to improve the effect and timeliness of ground fault monitoring and improve the effect of fault early warning, a DC ground fault monitoring method for electrical equipment in 110 kV smart substations based on improved rough sets is proposed. The characteristics of the DC ground fault current are analyzed, and the equivalent model of the DC system is constructed according to the analysis results. The low-frequency signal is enhanced according to the fuzzy rough set, and the low-frequency sampled transient zero-sequence current signal is enhanced preprocessing and wavelet packet decomposition according to the signal enhancement proportional coefficient, and the fault line is selected according to the principle of maximum energy. The wavelet packet transform method is used to decompose and analyze the waveform of the leakage current generated when a single-point grounding fault occurs in a branch of the DC system, and the grounding fault location of the DC system of the substation is realized by relative entropy operation. With the help of the cloud platform, a DC ground fault monitoring platform is built, and the ground fault monitoring is completed by using this platform. The experimental results show that the method can obtain the characteristic harmonics of the ground fault, accurately monitor whether the mutation occurs, and can realize the early warning of the ground fault of the electrical equipment in a faster time, indicating that it can realize the accurate and timely monitoring of the ground fault. [ABSTRACT FROM AUTHOR]

The industrial internet of things (IIoT) encompasses smart devices, manufacturing systems, humans, and networks for automated productive outcomes. The placement of devices and networks is vulnerable to distributed denial of service (DDOS) attacks that degrade the productivity and efficiency of IIoT. In this article, we propose a checkpoint‐intrigued adversary mitigation scheme (CIAMS) for improving the security features and recommendations of the detection systems. Features that use the recommendation to provide relevant information maintain their security level. A DDoS attack is dealt with at the outset, resulting in increased productivity. The IIoT's smart devices are less productive and efficient because of this DDoS attack. This CIAMS method is designed to address vulnerability and the ability to survive the features checkpoints. The proposed scheme substantiates the security breach and lag in the checkpoint systems against DDOS attacks. The checkpoints' vulnerability level and surviving features are assessed using a classified learning approach. In this assessment, the degrading features are reimbursed by improving the security functions, control, and access methods. Periodic checkpoint replacement and mutual security measures are used for mitigating the prolonging DDOS impact in the network. The proposed scheme's performance is verified using false positives, service distribution, lag, an efficiency score. Improvements have been made to the industrial environment's service delivery and efficiency. By reducing false positives by 10.35%, the proposed scheme improves service distribution ratio and efficiency score by 11.68% and 12.55% for different devices. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High-speed railway train control system has high safety level, various equipment components, complex functional structure and interface relationship, and great difficulty in dynamic inspection. By using the simulation test technology, it will completely rely on the on-site integrated commissioning and test, and be completed through the implementation of simulation test. It can further improve the test coverage and reduce the impact on transportation. On the basis of researches on the necessity of project construction, construction objectives and technical solutions, specific researches are carried out on ground equipment, on-board equipment, simulation system plan, etc. Through comparison and selection, the ground equipment of simulation testing platform for train control system engineering of the high-speed railway adopts the semi-physical and semi-simulation structure, and uses the cloud platform technology, which has better scalability and economy. The on-board equipment adopts the completely real equipment, which improves the credibility and authenticity of the test. Through the construction of simulation testing platform and the verification based on the simulation environment of Huzhou -- Hangzhou High-speed Railway, the research results show that compared with the field test, the simulation test has completed more scenarios, cases and route tests, and their numbers have increased by 275%, 336% and 442% respectively, which can further improve the quality, depth and breadth of the train control system test. [ABSTRACT FROM AUTHOR]