Intelligent Information Research Articles

Application and prospects of interface engineering in energy storage and conversion of graphdiyne‐based materials

AbstractA new carbon allotrope, graphdiyne (GDY) has great promise for future use. Much interest was piqued when it was initially prepared in 2010. GDY is made up of sp‐ and sp2‐hybridized carbon atoms. It has a one‐atom thick two‐dimensional structure and many interesting and useful qualities, such as strong chemical bonds, super‐large π structures, the ability to change from an alkyne to an alkene, and can be grown on any surface. GDY has become one of the frontier hotspots in chemistry and materials science, with original research achievements in energy conversion and storage, catalysis, intelligent information, life sciences constantly emerging and so on, showing revolutionary performance. In electrochemical cells, the electrode interface content not only accounts for a small proportion in the entire electrode system but it also plays a crucial role, affecting the efficiency, lifespan, power performance, and safety performance of the battery. In view of this, the intrinsic properties of GDY have been thoroughly analyzed, and a new GDY‐based electrochemical interface has been proposed by combining the key problems of electrochemical interfaces in electrochemical energy storage and conversion. This has led to new understanding and insights to address many critical scientific issues. In this review, the structure, characteristics, and applications of GDY in interface engineering are presented. In particular, recent advances in GDY and its aggregates in energy storage and conversion are summarized and discussed.

The design of intelligent highway transportation system in smart city based on the internet of things.

The design of intelligent expressway transportation system based on the Internet of Things is studied to improve the safety, travel experience, and operation management of expressway. The characteristics of the Internet of Things and cloud computing technology and its application on the expressway are analyzed, and the system design requirements of expressway intelligent transportation are understood. Besides, the overall architecture of the system is studied and designed. The IaaS layer, PaaS layer, and SaaS layer of the cloud platform are designed and deployed. The intelligent information system can make expressway highly informative. The simulation experiments reveal that the system only needs 120 milliseconds in accident processing time, which is far lower than the intelligent transportation system that only uses edge computing technology (201 milliseconds) and the intelligent transportation system that only uses cloud computing technology (443 milliseconds). Meanwhile, the accident response time is only 12s, which is also superior to other models. In terms of cost-effectiveness, the monthly cost of the system is 7004 yuan, with a CPU utilization rate of 53%, demonstrating good cost-effectiveness and resource utilization efficiency. In addition, compared with the existing system, the average traffic congestion time has been reduced by 25%, the traffic accident rate has been reduced by 18%, and the accident rate has been reduced by 27%. The intelligent traffic system design of expressway, expressway safety, travel service, and operation management is effectively improved by researching the intelligent traffic system design of expressway.

Acoustic cavitation assisted synthesis of PCDs@PVA composite film for UV shielding, intelligent pH detection, information encryption and food packing applications

Acoustic cavitation assisted synthesis of PCDs@PVA composite film for UV shielding, intelligent pH detection, information encryption and food packing applications

미래 학교 교육과정 체제로서의 다중심성 교육과정 생성·공유 체제

Objectives This study recognizes the need to explore a school curriculum system that responds to the changes brought about by the future hyper-connected networks, artificial intelligence era, and intelligent information society. As an alternative to the traditional Research, Development, and Dissemination (RDD) system of curricu-lum, we investigated the creation and sharing system of a polycentric curriculum. Methods To achieve the research objectives, we conducted two rounds of Delphi surveys with 50 education ex-perts, based on scenario construction for the future school curriculum system. Additionally, we carried out a per-ception survey targeting a total of 3,409 participants, including students, parents, teachers, and educational professionals. This was complemented by a semantic network analysis. Results The results of the first and second rounds of Delphi surveys with experts confirmed seven scenarios re-lated to the development and creation of future curricula. There was a high response rate of agreement on the an-ticipated scenarios related to curriculum development and creation in response to societal changes. Additionally, the semantic network analysis revealed that keywords such as individual student curricula, online networks, con-vergence and fusion curricula, online open markets, and digital and artificial intelligence technologies best repre-sent the characteristics of future curriculum development and creation. Conclusions In a future hyper-connected network society built on the integration of advanced digital information and artificial intelligence technologies, the traditional curriculum research, development, and dissemination sys-tem will transition to a polycentric curriculum creation and sharing system. Consequently, future curricula will be based on connected networks, establishing a unified system that will activate network-based curricula. This net-work curriculum system will form an educational ecosystem centered around educational stakeholders, function-ing through connected networks like a living organism.

The Change in Public Perception and Knowledge Acquisition Methods of Chronic Kidney Disease Among General Population in Okayama Prefecture, Japan

CKD public education plays a very important role in effective chronic kidney disease (CKD) countermeasure. We have been conducting CKD public education programs in Okayama Prefecture since 2007. Here, we aimed to examine the actual status of CKD perceptance and changes in CKD perceptance due to these education programs. The study was conducted on individuals who underwent health checkups at 12 medical institutions across five medical regions in Okayama Prefecture between 1 October and 30 November in 2015, 2019, and 2023. The results showed that overall CKD perceptance has improved over time (perceptance of “CKD” 4% to 7%, “chronic kidney disease” 27% to 34%, 2015 vs. 2023). “Chronic kidney disease” was more commonly recognized than “CKD”, and the elderly were more aware of the disease than younger people. The CKD perceptance improved across all age groups. However, the rate of CKD perceptance is still low, especially among young people. Previously, newspapers were the second most common resource of information about CKD after television. However, the Internet has recently replaced newspapers as the second most common source of information, especially among younger people. Understanding of the exact diagnosis of CKD also remains insufficient. It is necessary to continue more effective CKD public education programs through more intelligible terminology and information sources that match the demographics of target population.

Human risk recognition and prediction in manned submersible diving tasks driven by deep learning models

The complexity of human cognition is increased by the multiple and interactive information clusters brought about by the application of advanced intelligent information technologies. Especially in systems operating in extreme regions far from society, human errors are more pronounced than ever before. Prolonged social isolation, extreme weightless or overweight environments, stressful atmospheres, and lack of situational awareness are all added potential elements contributing to human risk. Although the development of human reliability analysis methods and their variants continues to mature, accurately predicting the potential risk of dynamic human behavior from sparse and discrete events remains a great challenge. We focus on deep learning computational architectures that are similar to the cognitive processes and mechanisms of the brain, and build neural networks that match the perceptual activation and memory cycling of the cognitive features of the brain. This study focuses on investigating the ability of the joint SNN-ITLSTM network to predict human error behavior and the clusters of performance shaping factors that effectively characterize the far-social nature. Combining the bionic properties of SNN and the temporal update mechanism of LSTM in the form of hierarchical events constitutes a computationally efficient network architecture. Our results show that the joint model proposed in this study has the performance to strengthen temporal influences and characterize cognitive features of the brain.

The effect of intersection vehicle scheduling based on the service system of sensors and intelligent traffic road information

With the high-speed development of the automobile industry, the number of domestic vehicles has increased significantly, which has posed a great challenge to the efficiency of road access. To fully utilize the intersection scheduling function of the intelligent transportation system, an optimization of the stranded traffic system based on LoRa is studied. Then, the greedy strategy and adaptive coefficient are used for optimization to obtain an improved genetic algorithm (GA), which is used to verify the intersection scheduling function. In addition, comparative experiments are conducted with standard GA, adaptive genetic algorithm (AGA), and hybrid genetic algorithm (HGA). The AGA optimizes the GA by adapting genetic parameters, while the HGA combines GA with a simulated annealing algorithm. The results showed that comparing the improved GA with GA, AGA, and HGA, the improved GA reduced the average value of individual extremum by 50.36%, 47.51%, and 37.16%, respectively. Compared with other mainstream algorithms, the improved GA reduced the number of stranded vehicles at intersections to 0 during traffic light timing. This indicates that the improved GA has higher and better performance and scheduling advantages in the intersection scheduling of intelligent transportation road service systems. The research aims to provide new ideas for improving traffic efficiency in intelligent transportation systems and promoting the informatization development of modern traffic management.

Design of a Universal Library of Components for the Development of Medical Systems in the Field of Thyroidology

Reducing the time and cost of developing application information systems by increasing the efficiency of the work performed is one of the main priorities of their modern production. With the development of AI technologies in all areas of activity, including medicine, there is a need for continuous improvement of both the client and server parts of information systems for many different nosologies. The purpose of this study is to form a ready-made library of components for the template design of user interfaces in the field of thyroidology (a field of medicine aimed at diagnosing and treating thyroid diseases) based on the experience gained in the development of several medical information systems for identifying thyroid diseases. Ensuring a conveyor mechanism for developing interfaces for medical information systems is possible only with specialized components of the tool based on a detailed understanding of the specifics of the actions of an endocrinologist. The components of the developed library cover up to 60 % of the component base of the interface of thyroidological systems, and are divided into several parts: for manipulating medical images and texts, annotating, creating input forms, working with tables of medical values, etc. The developed component library has been tested in the creation of intelligent information systems for the work of doctors of ultrasound and cytological diagnostics of thyroid diseases.

Falling behind again? Characterizing and assessing older adults' algorithm literacy in interactions with video recommendations

AbstractAlgorithms play a significant role in shaping our experiences of interacting with intelligent information systems but also inherit and amplify data biases, potentially leading to unfair decisions or discriminatory outcomes. This motivates us to investigate users' algorithm literacy, which covers the awareness and knowledge of algorithms and the skills to intervene in the operations of personalization algorithms when interacting with recommendation systems. Since vulnerable groups are more likely to suffer from the negative consequences of algorithmic decision‐making, investigating algorithm literacy among such groups is critical. This study aims to examine older adults' algorithm literacy, who are often considered a vulnerable group and labeled as digital laggards in contemporary information society. The empirical evidence collected from 21 participants in in‐depth interviews and cognitive mapping studies demonstrated that almost all participants are algorithm‐aware to some extent and identified (1) three types of information and sources collected by algorithms in user understanding, (2) two paradigms of how respondents understand personalized recommendations, and (3) two sets of strategies they develop to employ algorithms for improving user experience. The findings shed light on designing human‐centered intelligent information systems for unbiased personalization and developing a more inclusive AI‐assisted society that equally benefits people of all ages.

Empowering college physical education: AI-driven training, teaching, and intelligent information processing

The current status, methods utilized for Physical Education Training and Teaching System for College Students, and difficulties during information processing are all investigated in this comprehensive study. We compiled 130 empirical research on Artificial Intelligence-based Physical Education (AIPE) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses are analysed. Research shows that AI may improve health tracking, individualized training, and analysis of sporting performance. There is a lot of promise in AIPE for individualized lessons, immediate feedback, varied classroom settings, and evaluation. The problems arise when dealing with technological dependability, privacy concerns, and the need for instructor assistance. These results give light on important questions for future AIPE. This research delves into the process of creating and launching an all-encompassing educational platform that makes use of AI and data processing methods. Our system’s goals include improving the quality of instruction, tailoring feedback to each student, and enhancing the overall learning experience. AI Algorithms powered by artificial intelligence help us shift through student test scores, identify knowledge gaps, and modify lessons appropriately. This makes sure that the curriculum meets each student’s needs. Practical exercises, quizzes, and assignments get immediate feedback through the system. It uses natural language processing (NLP) to analyse student answers, find misunderstandings, and provide help for fixing them. The system personalizes learning routes according to students’ choices, learning styles, and progress. It suggests further reading, interactive games, and group assignments. Through the automation of administrative activities, generation of analytics reports, and suggestion of pedagogical changes, the system aids instructors. It makes it easier for students and instructors to talk to one another. Data protection, overcoming AI biases, and getting teachers on board with tech-enhanced lessons are all obstacles. Future studies should aim to improve the system, confirm its efficacy, and encourage its implementation across educational institutions. Finally, there is great potential for improving higher education with an AI-based training and teaching system with strong data processing skills. Students and teachers may reap the advantages of a technologically enhanced, ever-changing learning environment.

Integrity management system of hydrogen pipeline based on big data mining model optimized by improved whale algorithm

ABSTRACT In order to improve the integrity management level of the hydrogen pipeline, the intelligent information management system of the hydrogen pipeline based on big data intelligent mining model is proposed. The improved whale optimization algorithm is proposed by applying the adaptive weight update formula and quantum revolving door operation. Finally, a hydrogen pipeline is selected to carry out abnormal data detection, and the running time with different nodes and different clustering algorithms optimized by different intelligent algorithms is obtained, and the results show that the proposed Gaussian clustering optimized by an improved whale optimization algorithm can effectively find out abnormal data of integrity management of hydrogen pipeline. Monitoring precision of the proposed model is improved effectively, and the running time of the proposed model is also shortened; therefore, the proposed model can be an effective means for integrity management of the hydrogen pipeline.

Overtaking risk assessment based on extreme value theory and intelligent network information

In order to evaluate the overtaking risk level of intelligent connected vehicles when providing different connected information, and to make up for the neglect of driver factors in traditional risk assessment and the inadequate evaluation of complex traffic scenarios by a single traffic conflict indicator, this paper introduces the Block maxima (BM) and Peak over threshold (POT) methods to fit the extreme value distribution for two conflict scenarios involved in overtaking events (following vehicle conflict and frontal vehicle conflict), so as to evaluate the risk of following vehicle accidents and frontal collision accidents during overtaking. In each conflict scenario, a non-stationary extreme value model considering driver factors and a binary extreme value model considering different traffic conflict indicators are constructed, and the model is verified by the overtaking test data of intelligent connected vehicles in a two-way two-lane vehicle. Overtaking events were extracted from the original test data and conflict indicators were calculated: including the collision time GAP with the front vehicle at the beginning of the overtaking event, the collision time TTC_t1 with the oncoming vehicle, the deceleration DRAC, and the collision time TTC with the oncoming vehicle and the headway time THW with the front vehicle at the end of the overtaking event. The probability of the event with a negative time conflict indicator or DRAC greater than MADR was used to characterize the degree of collision risk. The results show that in the following vehicle conflict scenario, the binary extreme value model constructed with different conflict indicators has different error results, among which the binary extreme value model constructed with THW&DRAC has the most accurate evaluation result (standard error MAE = 0.00028); in the front vehicle conflict scenario, the binary extreme value model constructed with TTC&DRAC has the most accurate evaluation result ( MAE = 0.006 ). In different conflict scenarios, the non- stationary extreme value model considering the driver factor significantly improves the risk assessment accuracy compared with the model without considering the driver factor (small AIC and BIC values). In addition, different intelligent network information (real-time distance, overtaking advice, speed advice) brings different overtaking risks, and when the intelligent network information is speed advice, the vehicle's overtaking risk is the lowest. Therefore, the non- stationary extreme value model and binary extreme value model considering driver factors proposed in this paper can effectively evaluate driving risks through traffic conflict indicators. Secondly, the experimental data of intelligent networked vehicles show that the extreme value theory model proposed in this paper can accurately evaluate the overtaking risk level of intelligent networked vehicles when providing different network information.

Application and Research of Intelligent Multi-source Information Fusion Technology in Firefighting Equipment

As modern firefighting environments become increasingly complex, traditional firefighter equipment and operational methods are no longer adequate to address the high-risk challenges of fire rescue operations. Fire scenes are characterized by complex and hazardous environmental factors, with firefighters facing multiple threats such as extreme temperatures, dense smoke, and toxic gases. Therefore, ensuring the safety and efficiency of firefighters is of paramount importance. This study integrates multiple sensor technologies to enable real-time monitoring of firefighters' physiological status, posture changes, and fire scene conditions. The system incorporates ErgoLAB wireless ECG and PPG sensors, the BWT61CL posture sensor, and the MAG14mini infrared camera, which are capable of simultaneously capturing key physiological data and heat distribution in the fire scene. To ensure data accuracy and real-time performance, the study employs efficient signal preprocessing techniques, including noise reduction, baseline correction, and time synchronization. In addition, wireless transmission technology and multimodal data fusion algorithms are utilized to comprehensively analyze the firefighters' status and fire scene conditions. This approach significantly enhances the precision of firefighter safety monitoring and operational efficiency, demonstrating substantial innovation and practical value.

The Evolution of Intelligence Doctrine and Its Contribution to Maritime Security

The interconnected nature of the oceans and the absence of physical barriers along the maritime borders make it tough to control the movement of people and goods. Combating illicit activities at sea poses significant challenges that require cooperation between national security agencies and partner nations. To support decisionmaking in maritime security operations, it is crucial to establish a transparent maritime intelligence architecture based on cooperative techniques, knowledge sharing, and technological resources. Maritime security actors should collaborate to address gaps resulting from limited resources and the evolving modus operandi of nonstate threats. Therefore, a maritime intelligence architecture should outline the interaction between national and foreign agencies involved in collecting, analyzing, and sharing intelligence information. To meet those interoperability requirements, the article shows that the promotion of maritime security worldwide has led to the development of a new maritime intelligence doctrine based on cooperation and information sharing rather than the traditional “compartmentation of information” and “need to know” principles.

Distribution characteristics and development layout of rural tourism resources based on LSTM in-depth learning in the context of common prosperity

<p style="text-align:justify"><span style="font-size:10.5pt"><span style="line-height:150%"><span style="font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman","serif"">The development and layout of rural tourism resources is the core issue supporting the development and planning of tourism destinations. Currently, it mainly relies on the experience and judgment of planners, with strong subjectivity and lack of objective information technology support. Therefore, in the context of common prosperity, a method for distribution characteristics and development layout of rural tourism resource based on LSTM deep learning is proposed. After distinguishing various rural tourism resources using the classification method of rural tourism resources based on LSTM in-depth learning, the spatial distribution characteristics of various tourism resources are estimated using the analysis method for spatial distribution characteristics of tourism resources based on kernel density estimation; After identifying the current status of tourism resource functional areas through a method of tourism resource development and layout based on a grid spatial analysis model combined with the distribution characteristics information mastered, and from the perspective of tourists, it can optimize the layout mode of functional areas to maximize the benefits of rural tourism resources on the basis of satisfying tourists' preferences. After testing, this method can utilize intelligent information technology to optimize the development and layout of rural tourism resources.</span></span></span></span></span></span></p>

Enhancing a You Only Look Once-Plated Detector via Auxiliary Textual Coding for Multi-Scale Rotating Remote Sensing Objects in Transportation Monitoring Applications

With the rapid development of intelligent information technologies, remote sensing object detection has played an important role in different field applications. Particularly in recent years, it has attracted widespread attention in assisting with food safety supervision, which still faces troubling issues between oversized parameters and low performance that are challenging to solve. Hence, this article proposes a novel remote sensing detection framework for multi-scale objects with a rotating status and mutual occlusion, defined as EYMR-Net. This proposed approach is established on the YOLO-v7 architecture with a Swin Transformer backbone, which offers multi-scale receptive fields to mine massive features. Then, an enhanced attention module is added to exploit the spatial and dimensional interrelationships among different local characteristics. Subsequently, the effective rotating frame regression mechanism via circular smoothing labels is introduced to the EYMR-Net structure, addressing the problem of horizontal YOLO (You Only Look Once) frames ignoring direction changes. Extensive experiments on DOTA datasets demonstrated the outstanding performance of EYMR-Net, which achieved an impressive mAP0.5 of up to 74.3%. Further ablation experiments verified that our proposed approach obtains a balance between performance and efficiency, which is beneficial for practical remote sensing applications in transportation monitoring and supply chain management.

Development of an intelligent geographic information system for mountain roads monitoring: Ground data collection and analysis

This study examines the development of an intelligent geographic information system for mountain road monitoring. To make well-informed and timely management decisions in the planning, construction, and maintenance of mountain roads, and to anticipate potential critical situations and their effects on road conditions, it is imperative to employ modern methodologies. This study involves data collection from a specific section of a mountain road situated near Almaty, Kazakhstan. We gathered data on the road section through visual assessment, measurements of the coefficient of adhesion, and the acquisition of material samples from the structural layers of the roadbed. Based on the visual assessment and surface adhesion measurements, it became evident that the deformation of the studied road section, which includes a network of cracks and "non-standard" transverse cracks, is attributed to mountain mass shifts and water flows from slopes. Laboratory analysis of the collected samples revealed that excessive water saturation could result in cracks during freeze-thaw cycles, while a low resistance coefficient would lead to softening during humid conditions. An Intelligent Geographic Information System (IGIS) will incorporate the collected data alongside open remote sensing and weather data. The objective is to utilize this integrated system in the future for forecasting road conditions and maintenance requirements for similar roads in mountainous regions of the area. The measurements are carried out according to the state standards. That makes the system useful for government management purposes.

B-013 Improving the Workflow of Blood and Coagulation Tests in clinical laboratory through the Utilization of Laboratory Automation Track Systems

Abstract Background The Laboratory Automation Track System (TLA) is an automated system designed for executing highly repetitive tasks within a laboratory, commonly applied in biochemical and immunological testing systems. It aims to reduce human errors in specimen handling, enhance the overall process quality, and provide better Turnaround Time (TAT) from specimen collection to result reporting. For the first time in our laboratory, we have integrated the blood and coagulation systems with TLA. Consequently, we seek to evaluate the differences in the testing workflow and reporting efficiency of the blood and coagulation systems after being connected to TLA. Methods Abbott GLP systems Track was the TLA system for connecting with blood and coagulation systems. Two Sysmex XN-9100 fully automatic hematology systems and two Sysmex CS-5100 fully automatic coagulation analyzers were developed for blood and coagulation system, respectively. TUNYEN Laboratory Information System was established for Laboratory information system (LIS). Results Through the analysis of quality indicators and integration with intelligent information systems, improvements have been made, including the reduction of blood collection tube usage, efficient manpower utilization management, tracking of specimen progress and achievement of timely reporting rates.(1)Approximately 2,915 blood collection test tubes are saved every month, with a converted cost of 0.15 USD per tube, and a total saving of 437 USD. And reduce the carbon emissions of 32 kgCO2e, comply with the spirit of ESG sustainable development.(2)The TLA no longer requires staff to collect and deliver specimens as in the past,reducing staff and enhancing work efficiency.One of manpower was saved and new inspection items were developed.(3)Tracking of specimen progress:The laboratory has set up a specimen tracking system to monitor the specimen delivery location and provide timely response to the clinical status.The one-hour TAT for blood and coagulation increased from 82.0% and 76.9% before improvement to 91.8% and 93.3% after improvement respectively. Conclusions The implementation of Laboratory Automation Track Systems (TLA) leads to a reduction in medical errors and specimen sample volume, while also enhancing accuracy and precision, thereby improving the safety of laboratory staff. Although the introduction of TLA may require an initial investment, it brings the benefits of automation, offering ease and efficiency to laboratory processes.

Federating cross-domain BIM-based knowledge graph

Despite decades of the digitization of the construction industry, interoperability challenges prevail. One of which is the lack of sufficient integration between domain-specific BIM models. Therefore, the paper proposes a novel methodology for federating cross-domain BIM-based knowledge graphs. The approach leverages the integration of multiple models into a single knowledge graph, aiming to bridge the existing gaps in data exchange and enhance the semantic richness of building information models. Through a detailed exploration of existing data exchange mechanisms, popular ontologies, and schema limitations, this research addresses the interoperability dilemma. A practical demonstration, encapsulated in the “LBD-Online-Merge” demo application, validates the methodology’s efficacy in real-world scenarios, illustrating its potential to revolutionize building operations management and data preparation for the operational phase of a building’s lifecycle. This study significantly contributes to the academic and practical discourse by providing a robust framework for achieving a more integrated, intelligent building information management ecosystem, setting a new benchmark for future research and applications in constructing semantically rich digital twins of the built environment.

Intelligent Recognition and Information Extraction of Radar Complex Jamming Based on Time-Frequency Features

Intelligent Recognition and Information Extraction of Radar Complex Jamming Based on Time-Frequency Features