Search

Unmanned aerial vehicles (UAVs) assisted Internet of things (IoT) data collection is an efficient and promising approach. The optimization of resource allocation in path planning is addressed in this paper by refining the energy consumption model and considering three metrics: the amount of collected data, time efficiency, and energy efficiency. The problem is formulated as a distributed partially observable Markov decision process (POMDP) and a novel deep reinforcement learning algorithm called RISE (Rényi state entropy)-D3QN (dueling double deep Q network) is proposed. It combines intrinsic rewards, prioritized experience replay, and soft-max exploration strategy, enabling path planning for UAV swarms while adapting to changes in UAV battery capacity, IoT device locations, data volume, and quantity. Simulation results demonstrate that compared to traditional D3QN and DQN algorithms, the proposed approach significantly increases. the amount of collected data from IoT devices while reducing UAV flight time and energy consumption, all while ensuring UAV safety during flight. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute 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.)

Data collection is the primary link in the simulation execution process. The integrity and efficiency of data acquisition have a significant impact on the final effect and efficiency of the entire training simulation activity. However, in the existing distributed simulation system based on high level architecture (HLA), centralized data collection is difficult to handle massive data in a single step, which will affect the normal advancement of simulation while distributed data collection will cause a large number of redundant data, and the development of the collection module does not have universality. In response to the above problems, based on weak distributed data acquisition structure, maniple collection members are used to realize parallel data collection, and the distribution strategy of collection tasks in formulated among multiple members by the non-dominated sorting genetic algorithm-II (NSGA-II) to achieve a balanced distribution of data collection loads. The experimental results on simulation result and real system show that the proposed method can significantly improve the efficiency of data collection while reducing the central processing unit (CPU) and memory consumption during the execution of data collection members. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal 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.)

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics 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.)

With the development of Industry 4.0 and the Industrial Internet, the mining industry has also begun to move towards intelligence. Faced with the current situation of complex access terminals, diverse communication protocols, access networks and heterogeneous multi-source data in the coal mine industry, an intelligent mine IoT edge data gateway technology is proposed. By constructing an edge gateway management architecture, the northbound uplink connects to the cloud platform using transmission protocols such as TCP, HTTP, WebSocket, MQTT, etc., and the southbound downlink supports multiple communication interfaces to connect perception layer devices, achieving protocol conversion of different types of networks and unified access of multi-source heterogeneous data from various systems. The research results have been applied for data collection and forwarding automation related equipment in multiple coal mines. [ABSTRACT FROM AUTHOR]

Copyright of Electric Drive for Locomotives is the property of Electric Drive for Locomotives 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.)

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China 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.)

In order to improve network routing performance, a QoS (quality of service) multicast routing algorithm based on genetic ant colony optimization algorithm was proposed and designed. Firstly, an adaptive frequency conversion acquisition strategy was designed to collect network and node information, in order to obtain the status of the network and nodes and provide data support for subsequent routing optimization. Secondly, the path cost was calculated, the minimum path cost as the optimization objective was set, a QoS multicast routing optimization model was established, and relevant constraints was set. Finally, a genetic ant colony optimization algorithm was proposed by combining genetic algorithm and ant colony algorithm to solve the above model, output the optimal path, and complete routing optimization. The experimental results show that the proposed algorithm can effectively reduce path length and cost, improve search efficiency and routing request success rate, and optimize the routing delay jitter [ABSTRACT FROM AUTHOR]

Copyright of Journal of Petrochemical Universities / Shiyou Huagong Gaodeng Xuexiao Xuebao is the property of Journal Editorial Department Of Liaoning Shihua University 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.)

Aiming at the current problem of insufficient security in clustering data collection and publication, in order to protect user privacy and improve data quality in clustering data, a privacy protection method for K-Modes clustering data collection and publication was proposed without trusted third parties based on the shuffled differential privacy model.K-Modes clustering data collection algorithm was used to sample the user data and add noise, and then the initial order of the sampled data was disturbed by filling in the value domain random arrangement publishing algorithm.The malicious attacker couldn’t identify the target user according to the relationship between the user and the data, and then to reduce the interference of noise as much as possible a new centroid was calculated by cyclic iteration to complete the clustering.Finally, the privacy, feasibility and complexity of the above three methods were analyzed from the theoretical level, and the accuracy and entropy of the three real data sets were compared with the authoritative similar algorithms KM, DPLM and LDPKM in recent years to verify the effectiveness of the proposed model.The experimental results show that the privacy protection and data quality of the proposed method are superior to the current similar algorithms.

To improve the efficiency of large-scale Internet of things (IoT) data collection, a coordinated trajectory planning algorithm for multiple aerial and ground vehicles based on load balancing region partitioning was proposed, where unmanned aerial vehicles (UAVs) acting as aerial base stations were dispatched to gather data from IoT devices and unmanned ground vehicles (UGVs) acting as mobile battery swap stations were used to compensate for the shortage of UAV’s energy.Aiming at shortening the mission completion time, the optimization task was to minimize the longest mission time among a fleet of UAV-UGVs, which was formulated as a variant of min-max multi-depot vehicle routing problem and solved from the load-balancing perspective.Specifically, the IoT devices were assigned to the UAV-UGVs’ service zones by a load-balancing region partition algorithm, based on which the trajectory planning problem of multiple UAV and UGV was reduced to several independent route planning problems for each UAV-UGV pair.Then, a cooperative trajectory planning strategy was developed to optimize the route in each service zone.Numerical results validate that the proposed algorithm outperforms the compared algorithms in terms of mission completion time and balancing degree.

[Purpose/Significance] Data empowerment for collaborative transformation of the entire industrial chain has become a national strategy. In the context of multi-source and heterogeneous big data, we face higher demands on the diversity and real-time of recommendation services, as well as the standardization and comprehensiveness of user and item information management, in order to turn data into resources, support the diversification of smart recommendation services, and enhance the user interaction experience. To this end, we should systematically sort out and analyze the research results of user interest modeling and item information management technologies in China's big data empowerment, elaborate the technical system for standardized management of user and item information in intelligent recommendation. [Method/Process] The article summarizes the content of 507 documents using literature review methods, and summarizes and analyzes the domestic big data empowerment resource management technology for smart recommendation from two aspects: user interest modeling and item information management. User interest modeling technology includes model representation, model initialization and model evolution, the item information management technology shows the goals, implementation methods, implementation techniques, and methods of item information management, including data collection, data mining, data storage, data updating, and data interpretation. [Results/Conclusions] First, the user interest modeling technology of big data empowerment mainly studies the following issues. 1) The method of model representation is to accurately formalize the user's needs. 2) The model is initialized to collect user information in an implicit or explicit way and select tools and technologies according to data types to pre-process user data according to a fixed process, and select measurement methods to quantify user interest. 3) Model evolution aims to apply methods and technologies to evolve user interest models offline and online. Second, the item information management technology of big data empowerment mainly studies the following issues. 1) The online technology and offline equipment are used to synchronously collect item data across the whole domain, integrate domain knowledge, and use algorithm libraries and data analysis methods to mine item data. 2) The distributed storage is used such as databases and file systems to store item data. 3) Data collection, mining, and storage technologies are the main techniques, item data are updated through offline and online methods, and visualization technology is used to intuitively interpret ietm data. Finally, existing research needs to strengthen the design and practical research of user-driven and data-driven smart recommendation solutions, as well as strengthen research on data security.

Aiming at some "pain points" in data access of the wargame system, a bus-based real-time data collection and management platform for wargame is designed by absorbing the experience and lessons of domestic and foreign wargame systems. The distributed storage platform is introduced as the underlying storage foundation, and the in-memory database is introduced as the external service interface. The bus-based data collection is used as the collection source, and data cleaning and branch management based on data segments are performed. The system as a whole is divided into three modules, namely collection module, management module, and service module. It realizes real-time collection, cleaning, storage and management of wargame data. The practical application shows that the platform provides high-speed and reliable data access support for other modules in the wargame system.

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications 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.)

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications 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.)

[Purpose/Significance] Data empowerment for collaborative transformation of the entire industrial chain has become a national strategy. In the context of multi-source and heterogeneous big data, we face higher demands on the diversity and real-time of recommendation services, as well as the standardization and comprehensiveness of user and item information management, in order to turn data into resources, support the diversification of smart recommendation services, and enhance the user interaction experience. To this end, we should systematically sort out and analyze the research results of user interest modeling and item information management technologies in China's big data empowerment, elaborate the technical system for standardized management of user and item information in intelligent recommendation. [Method/Process] The article summarizes the content of 507 documents using literature review methods, and summarizes and analyzes the domestic big data empowerment resource management technology for smart recommendation from two aspects: user interest modeling and item information management. User interest modeling technology includes model representation, model initialization and model evolution, the item information management technology shows the goals, implementation methods, implementation techniques, and methods of item information management, including data collection, data mining, data storage, data updating, and data interpretation. [Results/Conclusions] First, the user interest modeling technology of big data empowerment mainly studies the following issues. 1) The method of model representation is to accurately formalize the userfs needs. 2) The model is initialized to collect user information in an implicit or e,q)licit way and select tools and technologies according to data types to pre-process user data according to a fixed process, and select measurement methods to quantify user interest. 3) Model evolution aims to apply methods and technologies to evolve user interest models offline and online. Second, the item information management technology of big data empowerment mainly studies the following issues. 1) The online technology and offline equipment are used to synchronously collect item data across the whole domain, integrate domain knowledge, and use algorithm libraries and data analysis methods to mine item data. 2) The distributed storage is used such as databases and file systems to store item data. 3) Data collection, mining, and storage technologies are the main techniques, item data are updated through offline and online methods, and visualization technology is used to intuitively interpret ietm data. Finally, existing research needs to strengthen the design and practical research of user-driven and data-driven smart recommendation solutions, as well as strengthen research on data security. [ABSTRACT FROM AUTHOR]

Aiming at some "pain points" in data access of the wargame system, a bus-based real-time data collection and management platform for wargame is designed by absorbing the experience and lessons of domestic and foreign wargame systems. The distributed storage platform is introduced as the underlying storage foundation, and the in-memory database is introduced as the external service interface. The bus-based data collection is used as the collection source, and data cleaning and branch management based on data segments are performed. The system as a whole is divided into three modules, namely collection module, management module, and service module. It realizes real-time collection, cleaning, storage and management of wargame data. The practical application shows that the platform provides high-speed and reliable data access support for other modules in the wargame system. [ABSTRACT FROM AUTHOR]

Objective To explore the impact of different comorbidity patterns on the quality of life of community residents aged 55 years and above, and to provide reference for the early intervention of comorbidity and the improvement of population quality of life.Methods Using cluster sampling method, five urban communities with relatively high proportion of elderly population in Wuzhong city and Yinchuan city of Ningxia were selected.The one-to-one questionnaire survey and physical examination were completed in the form of face to face.Different comorbidity patterns such as Charlston comorbidity, cardiovascular-metabolic comorbidity, cardiovascular-metabolic comorbidity depression, and cardiovascular-metabolic comorbidity mild cognitive dysfunction were used to analyze the impact of different comorbidity patterns on the quality of life of middle-aged and elderly people in the community using a multivariate logistic regression analysis model after controlling for the general situation.Results Physiological function（PF）,physiological function（RP）,social function（SF）,body pain（BP） and the total score of quality of life rating scale（SF-36） were significantly different in the Charlson comorbidibility index aCCI≥3 scores group and the aCCI＜3 scores group（P＜0.05）.The dimensions of PF,RP,RE and BP had statistical significance in the whether suffered from cardiovascular metabolic comorbidity group（P＜0.05）.The dimensions of PF,RP,Role-Emotional（RE）,SF,BP and SF-36 score were significantly different in the whether suffered from cardiovascular metabolic comorbidity depression group（P＜0.05）.There were significant differences in the dimensions of PF,RP,RE,SF,BP and the total score of SF-36 in the whether suffered from cardiovascular metabolic comorbidity depression group（P＜0.05）.The logistic regression model analysis showed that women[odds ratio（OR）=1.88,95% confidence interval（95%CI） 1.341-2.632],primary school education or below（OR=1.31,95%CI 1.009-1.700） had a higher risk of declining quality of life score.After controlling for general condition variables, aCCI≥3 scores（OR=1.43,95%CI 1.075-1.914）,cardiovascular metabolic comorbidities（OR=1.37,95%CI 1.022-1.847）,cardiovascular metabolic disease comorbidity depression（OR=2.19,95%CI 1.620-2.960） had a high risk of decline in quality of life scores, and no association was found between cardiovascular metabolic disease comorbidities with mild cognitive impairment and decline in quality of life（P＞0.05）.Conclusion Different comorbidity patterns may have a certain impact on the quality of life of middle-aged and elderly people in urban communities.Attention should be paid to early intervention of elderly comorbidity to provide reference for improving the quality of life of the population. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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.)

Copyright of Chinese Journal of Inorganic Analytical Chemistry / Zhongguo Wuji Fenxi Huaxue is the property of Beijing Research Institute of Mining & Metallurgy Technology Group 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.)

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal 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.)

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong 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.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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.)

Background The past nearly 20-year period has seen a sudden increase in the use of artificial intelligence (AI) in esophageal cancer research, and an emergence of many systematic reviews and meta-analyses of the research. However, most of the reviews and meta-analyses only address a single aspect in summary, making it difficult for researchers to gain a comprehensive understanding of the latest developments and research hotspots in the field. Objective To perform a bibliometric analysis of the use of AI in esophageal cancer research, and the development, hotspots and emerging trend in this field. Methods All literature in English regarding esophageal cancer research using AI included in the Science Citation Index Expanded database of the Web of Science Core Collection was searched from 2000-01-01 to 2022-04-06. Microsoft Excel 2019, CiteSpace (5.8R3-64bit) and VOSviewer (1.6.18) were used to analyze the literature for annual number of publications, country, author, institution, co-citation and keywords. Results Nine hundred and eighteen studies were retrieved, with a total of 23 490 times of being cited. The number of studies published between 2000 and 2016 grew slowly (from 6 to 40), but increased rapidly between 2017 and 2022 (from 62 to 216). Sixty countries, 118 institutions and 5 979 authors were involved in the studies. China (306 articles), the United States (238 articles) and the United Kingdom (113 articles) ranked the top three in terms of number of studies published. The top three institutions in terms of intensity of cooperation were University of Amsterdam (TLS=72), Catherine Hospital (TLS=64) and Eindhoven University of Technology (TLS=53). The top three authors in terms of number of publications were Jacques J G H M Bergman from the Netherlands (n=16), Tomohiro Tada from Japan (n=12), and Fons Van Der Sommen from the Netherlands (n=12). There were 39 962 co-cited authors and 42 992 co-cited studies. Thirty-three burst keywords were identified: the major burst keywords were p53 and mutations in 2001-2008 (early stage), and were esophageal cancer classification, new examination techniques (tomography), differentiation, identification and comparison between esophageal cancer and other cancers in 2013-2018 (middle stage), and were deep learning, convolutional neural network, and machine learning in esophageal cancer examination and diagnosis applications in 2019-2022 (late stage). Among which deep learning had the highest burst intensity (burst intensity of 13.89) . Conclusion AI application in esophageal cancer research has entered a new phase, moving gradually from genes and mutations toward accurate examination, diagnosis, and treatment. The latest major burst keywords in recent years (2019-2022) are deep learning, convolutional neural network, and machine learning in esophageal cancer examination and diagnosis. The future challenges to the use of AI in esophageal cancer research may include individual data collection, data quality assurance, data processing specifications, AI code reproduction, and reliability assurance of AI-assisted diagnostic decision-making.

[Purpose/Significance] Since the outbreak of COVID-19, there has been a rapid increase in the number of studies related to COVID-19 at home and abroad. Review of relevant literature on COVID-19 provides data resources for related research on the emergence and transmission mechanism of SARS-CoV-2. However, the current COVID-19 related dataset is a collection of the literature, without classifying the data for each subfield, and the coarse-grained information such as the title and author fails to provide an in-depth understanding of the progress of COVID-19 research. Therefore, this paper created a dataset for the COVID-19 sub-domain and a fine-grained entity dataset. [Method/Process] Firstly, this paper proposed a literature screening method based on active learning model, which can obtain more valuable marker samples with less labor cost, so that the classifier has better generalization performance. We considered three base classifiers: Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF), while considering four query strategies: uncertainty sampling, expected error reduction, committee-based query, and random sampling. Taking the origin of SARS-CoV-2, one of the sub-fields related to SARS-CoV-2, as an example, articles related to the origin of SARS-CoV-2 were efficiently and accurately located from the literature. At the same time, this paper designed a labeling scheme covering 18 types of entities, including not only genes, proteins, compounds and other entities that are universal in the biological field, but also corona viruses and wild animals that are unique to the field of SARS-CoV-2. In this paper, visual annotation tool BRAT was used for entity annotation. The tagging team consisted of an administrator and six annotators, and the entity tagging consisted of two rounds. What's more, multi-k consistency index was used to calculate the consistency score of annotation results. [Results/Conclusions] The results of the active learning model show that the uncertain sampling query strategy has the best performance. SVM, LR and RF based on uncertain sampling can correctly screen 425, 465 and 489 articles, respectively. After the removal of overlapping articles, a dataset related to the origin of SARS-CoV-2 was constructed, containing a total of 885 articles. Secondly, based on the proposed entity labeling scheme, 6 annotators completed 99 papers. Based on the results of fine marking, this paper constructed an entity dataset containing 39,118 entities, which is the largest and most comprehensive entity corpus in the field of COVID-19.

Background Noncommunicable diseases (NCDs) are increasingly prevalent in Chinese residents, and are happening younger and younger, which have become a major threat to population health. Objective To analyze the characteristics and trends of premature deaths due to major NCDs among Shenzhen permanent residents aged 30-69 from 2014 to 2020, providing a basis for the formulation of chronic disease prevention and control policies. Methods The cause-of-death data of Shenzhen permanent residents (30-69 years old) came from the all-cause-of-death surveillance system operated by the Shenzhen Center for Disease Control and Prevention. The causes were classified into four major NCDs including cancer (C00-C97), diabetes (E10-E14), cardiovascular and cerebrovascular diseases (I00-I99), and chronic respiratory disease (J30-J98) in accordance with the International Classification of Diseases, Tenth Revision (ICD-10). Crude mortality, age-standardized mortality and probability of premature death were used for analyzing deaths due to the four above-mentioned NCDs. The annual percentage change (APC) was used to measure the temporal trend of each index. Results During 2014-2020, no significant changes were found in the overall trends of crude mortality and age-standardized mortality of the four major NCDs (APC=-2.70%, P>0.05; APC=-2.00%, P>0.05), while the proportion of deaths due to the four major NCDs of all deaths (76.58% -82.63%) showed a significantly increasing trend (APC=1.20%, P 0.05), diabetes (APC=3.80%, 2.20%; P>0.05) and chronic respiratory diseases (APC=-1.30%, -0.60%; P>0.05) changed insignificantly, so did the trend of age-standardized mortality of them, while the crude mortality and age-standardized mortality (34.22%-48.85%) of cardiovascular and cerebrovascular diseases showed a notable decrease (APC=-4.70%, -4.60%; P0.05). But the trend in the probability of premature death caused by cardiovascular or cerebrovascular disease showed an obvious decrease (APC=-5.10%, P

A rechargeable unmanned aerial vehicle (UAV) aided wireless sensor network was considered, which consists of multiple ground terminals with a large amount of time-sensitive data to be collected.Due to the limited battery capacity, the UAV cannot collect the data from all terminals through a single flight mission, and it needs to return to the charging pile to replenish its flight energy several times during the whole mission.The optimization of the terminal scheduling, trajectory, flight speed and transmission rate for the UAV was studied to maximize the number of terminals whose data had been collected within the data lifetime limit.Due to the variable coupling and the existence of discrete binary scheduling variables, the considered optimization problem is difficult to solve.To tackle such a difficulty, an efficient algorithm was proposed based on the stochastic optimization and the feature engineering.Specifically, the flight hover communication protocol was introduced to simplify the UAV flight process.And then a terminal scheduling algorithm was innovatively proposed with the influence factor and the stochastic preference, which extracted the features that affect the service time of the UAV, optimized the weights of the features, and further simplified the optimization problem into multiple subproblems.The subproblems were then solved by using the block coordinate descent and successive convex approximation techniques.Simulation results show that the proposed optimization algorithm achieves significant performance gains over several benchmark schemes in the scenarios with different data lifetime requirements and different numbers of ground terminals.

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING 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.)

In view of the current coal mine safety supervision problems of insufficient real-time and pertinence, relying on the basic data support of “comprehensive management and control platform” in the intelligent construction of coal mine, this paper proposes the supervision concept of using information technology to build coal mine safety production risk monitoring and early warning system. This paper analyzes three forms of existing coal mine safety risk data integration, takes the special topic of fire risk in coal mine goaf as an example, and explains the real-time supervision mode of major risk special topic with four risk indexes of blue, yellow, orange and red. Taking an eastern province as an example, the intelligent supervision mode based on coal mine safety production risk monitoring and early warning system is verified, which realizes the remote real-time supervision of some functions of mine end supervision and the drilling query of important safety index data, and greatly improves the real-time and pertinence of coal mine safety supervision and supervision.

Background The percentage of older population of the total population is increasing in developed areas of developing countries, and the impact and contribution of population composition factors towards cancer mortality are still unclear. Objective To analyze the epidemiological characteristics and trend of cancer mortality among community-living residents in central Pudong New Area of Shanghai from 2007 to 2020. Methods From January to December 2021, we retrospectively collected the death data during 2007 to 2020 of registered permanent residents living in Dongming Road Community of Pudong New Area from the all-cause death monitoring database, and population composition data from Pudong New Area Public Security Bureau. We used the crude mortality rate (CMR) , standardized mortality rate (SMR, with reference to the SMR of the general Chinese population in 2010) , and average annual percent change (AAPC) to analyze the status and trends of cancer mortality, and used the decomposition method to estimate the contribution rates of demographic and non-demographic factors to the change of cancer mortality. Results From 2007 to 2020, a total of 2 446 cancer-induced deaths were reported by the Dongming Road Community, accounting for 28.48% of the total reported deaths (8 589 cases) . The CMR of cancers was 268.56/105, in which the CMR due to lung cancer, colorectal cancer, and gastric cancer was 69.61/105, 33.05/105 and 31.84/105, respectively. The SMR of cancers was 71.46/105 , in which the SMR due to lung cancer, colorectal cancer, and gastric cancer was 33.12/105, 15.46/105, and 15.29/105, respectively. During this period, the CMR for cancers among men, women and the whole population remained stable (Z=1.82, -0.54, 0.87, P>0.05) . The CMR and SMR in men were higher than those in women (u=6.69, P0.05) . As in the whole population, it showed a downward trend year by year (AAPC=-1.73%, Z=-3.09, P=0.010) . Furthermore, the CMR for cancers in those aged 65-79 showed a downward trend gradually (AAPC=-4.17%, Z=-1.80, P

Objective To find out the comprehensive situation of young medical talents and provide feasible suggestions for accelerating the cultivation and construction of young medical talent team. Methods The overall situation of young medical talent team was collected by personnel, scientific research, teaching and other departments, and the difficulties of young medical talents′ development were collected by questionnaire. Results Young medical talents had gained in scientific research, teaching, talent projects or titles, but there were also some problems, such as high work pressure, lack of learning opportunities and research team, and so on. Conclusion Uphold the principle that the Party oversees talents, class the training of talents exactly, innovate talent training mechanism, strengthen the high-end talent team and improve the discipline talent training, so as to make better service for children′s health and social development. [ABSTRACT FROM AUTHOR]

Objective To understand medical students′ cognition and education status of medical risks. Methods The method of convenience sampling was used to conduct a questionnaire survey among 618 undergraduates in a medical university. Results More than 70% of medical students believed that “medical activities are inherently risky” and “there are medical risks in the medical industry”. The top four risks among the medical risk types known to medical students were “improper operation and invasive operation”（85. 44%),“nosocomial infection”（83. 50%),“improper use of medical equipment”（80. 26%),“diagnosis error, delayed treatment”（72. 33%). 31. 39% of medical students′ scores of known the medical risk over 10 points, and there were still 16. 50% of medical students scored less than five points. About 30% of medical students said that medical risks would have an impact on their interest in learning and future planning. “Carrying out online and offline lectures and consultations”（65. 70%) was the most inclined for medical students of medical risk education. Conclusion Medical schools should adopt a combination of online and offline methods to strengthen the popularization of medical risks for medical students, and enrich the legal knowledge and clinical experience of medical students through the construction of theoretical courses and practice platforms, strengthen the learning of clinical professional knowledge, improve doctor-patient communication skills, thereby enhancing medical students′ awareness of medical risks and the ability to effectively avoid medical risks, and prevent problems before they occur. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] Since the outbreak of COVID-19, there has been a rapid increase in the number of studies related to COVID-19 at home and abroad. Review of relevant literature on COVID-19 provides data resources for related research on the emergence and transmission mechanism of SARS-CoV-2. However, the current COVID-19 related dataset is a collection of the literature, without classifying the data for each subfield, and the coarse-grained information such as the title and author fails to provide an in-depth understanding of the progress of COVID-19 research. Therefore, this paper created a dataset for the COVID-19 sub-domain and a fine-grained entity dataset. [Method/Process] Firstly, this paper proposed a literature screening method based on active learning model, which can obtain more valuable marker samples with less labor cost, so that the classifier has better generalization performance. We considered three base classifiers: Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF), while considering four query strategies: uncertainty sampling, expected error reduction, committee-based query, and random sampling. Taking the origin of SARS-CoV-2, one of the sub-fields related to SARS-CoV-2, as an example, articles related to the origin of SARS-CoV-2 were efficiently and accurately located from the literature. At the same time, this paper designed a labeling scheme covering 18 types of entities, including not only genes, proteins, compounds and other entities that are universal in the biological field, but also corona viruses and wild animals that are unique to the field of SARS-CoV-2. In this paper, visual annotation tool BRAT was used for entity annotation. The tagging team consisted of an administrator and six annotators, and the entity tagging consisted of two rounds. What's more, multi-k consistency index was used to calculate the consistency score of annotation results. [Results/Conclusions] The results of the active learning model show that the uncertain sampling query strategy has the best performance. SVM, LR and RF based on uncertain sampling can correctly screen 425, 465 and 489 articles, respectively. After the removal of overlapping articles, a dataset related to the origin of SARS-CoV-2 was constructed, containing a total of 885 articles. Secondly, based on the proposed entity labeling scheme, 6 annotators completed 99 papers. Based on the results of fine marking, this paper constructed an entity dataset containing 39,118 entities, which is the largest and most comprehensive entity corpus in the field of COVID-19. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of data collection, nearest neighbor-based trajectory planning of unmanned aerial vehicle (UAV) for data collection (NNDC) is proposed in this paper. In NNDC algorithm, energy of node is supplied through wireless power transfer (WPT) from UAV. Then the working time of node is extended. Based on the constraints of energy consumption of UAV and ensuring the collection data of each node, the objective function of minimizing time of collecting data is established, which is solved by nearest neighbor algorithm. Then, the optimal trajectory of collecting data is obtained to realize the UAV trajectory planning. Performance analysis shows that the proposed NNDC algorithm outperform the similar algorithm in terms of time of collecting data and energy consumption of UAV in data collection process. The average time of collecting data is less than 100 s when the number of nodes is from 6 to 20. [ABSTRACT FROM AUTHOR]

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING 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.)

The article summarizes the collection and management of data from scientific research and introduced technical details. Analysis covers the general characteristics of 29 projects from studies of basic medicine in 2020, which shed light on overall understanding of the scientific data collection process and its implementation. In addition, this study substantially facilitates capacity building for gathering, processing and managing of research data while promoting open sharing. circulation, utilization of data and yielding added-value.

At present, there are some problems in the field of locomotive maintenance data analysis,such as large volume of onboard data,low efficiency, data loss and analysis omission. Based on the 5G communication technology, using the methods such as vehicle-ground integration IoT data collection and transmission, big data intelligent analysis and linkage analysis, this paper proposes a multi-dimensional integrated maintenance data application system which integrates the functions of vehicle-mounted system, ground transmission control system and the application information comprehensive intelligent analysis system. The system has the functions of vehicle-mounted multi-system data acquisition, high speed dumping of vehicle-ground data, data encryption transmission, continuous transmission of massive data at breakpoints, multi-system linkage analysis, automatic data association, audio and video intelligent analysis, and big data visualization analysis. The on-site application proves that full automatic and encrypted data transmission of the system avoid security risks such as data loss and leakage. At the same time, the intelligent analysis system realizes 100% analysis coverage of the dumped data of locomotives. Under the premise of halving the number of analysts and more than 10 times amount of analysis data increase, it achieves the effect of liberating manpower, improving work efficiency and fault-identification accuracy.

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) 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.)

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong 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.)

Copyright of Cotton Textile Technology is the property of Cotton Textile 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.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering 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.)

The rapid control prototype technology is introduced into the experimental teaching of intelligent electric apparatus in this paper. Taking the NI-CompactRIO as the core and the contactor, a typical apparatus as the object, the hardware and software design methods of the rapid control prototype system are introduced. It not only realizes the closed-loop control of the contactor, but also can collect large-scale operation data. The human-machine interface of the system is friendly, intuitionistic and interactive. It can easily rewrite the control scheme, change the control parameters in real time, and present the dynamic process waveforms synchronously, which is convenient for students to fully analyze and understand the control principle of intelligent electric apparatus. The system can be used as a good platform for experimental teaching of intelligent electric apparatus. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Northeast Petroleum University is the property of Journal of Northeast Petroleum University 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.)

In view of the needs that coal mine risk monitoring and early warning needs to establish the real-time data collection and calculation at the edge, and storage, manage, analyze and early warn mass data at cloud, it is proposed to establish an integrated coal mine risk monitoring and early warning platform based on edge and cloud to provide services for coal mine accident risk monitoring and early warning as well as remote supervision of coal regulatory agencies. An integrated coal mine risk monitoring and early warning platform is designed, which is composed of the coal mine, the edge data resource layer of the coal supervision bureau, the cloud platform service layer and the application layer. It focuses on the data interaction between the edge end and the cloud, and the functions of the risk monitoring and early warning application system. The application results show that the system is stable and reliable, which can effectively improve the monitoring and early warning of coal mine accident risk, the remote supervision, the precise law enforcement ability of the supervision department, and improve the law enforcement efficiency.

The traditional acquisition process of environmental parameters is easily affected by environmental noise, which leads to prolonged data transmission time and low data integrity. To solve this problem, with the STM32 processor adopted, the alarm circuit was used to avoid the problem of too large or too small data collection, and then the anti-interference design of the system was realized through the configuration of decoupling capacitor and the design of ground wire, so as to reduce the data transmission delay. Based on this, Kalman filtering algorithm could be used to restore the performance of real data, remove the environmental noise of communication link, and realize complete parameter acquisition. The experimental results show that the data transmission delay of the system in this paper can be kept below 15 s and the effective integrity of the parameter data is always not less than 0.96 regardless of single hop or multi hop transmission.

With the continuous development of Internet technology and the continuous expansion of network scale, there are many different types of applications , and various new applications have endlessly emerged.In order to ensure the quality of service (QoS) and ensure network security, accurate and fast traffic classification is an urgent problem for both operators and network managers.Firstly, the problem definition and performance metrics of network traffic classification were given.Then, the traffic classification methods based on machine learning and deep learning were introduced respectively, the advantages and disadvantages of these methods were analyzed, and the existing problems were expounded.Next, the related work by focusing on the three problems encountered elaborated and analyzed in traffic classification when considering online deployment: dataset, zero-day application identification and the cost of online deployment, and further discusses the challenges faced by the current network traffic classification researches.Finally, the next research direction of network traffic classification was prospected.

The implementation of open government data (OGD) initiatives has become a worldwide phenomenon over the last decade. While there have been many studies exploring this phenomenon, few have focused on data collection phase in data opening process, during which government officials seek government-owned datasets for OGD purposes. To establish a model for the process of data collection phase, this study conducted semi-structured interviews with fifteen participants recruited through purposive and snowball sampling methods from eleven central and local Taiwanese government agencies. This paper discusses the research findings regarding (1) why government officials undertake tasks of data preparation, (2) how potential datasets are discovered, selected, and obtained from multiple government agencies, and (3) how information is sought to facilitate tasks of data preparation. These findings bridged the research gap in the study of data opening process and verified previous information seeking models. Suggestions for future practitioners and topics worthy of further exploration are enumerated in the conclusion section.