Manual Management Research Articles

Design of intelligent visitor system based on cloud and edge collaborative computing

With the maturity of face recognition and speech recognition technologies, artificial intelligence (AI) cloud and edge computing collaboration have become a new research direction. In many enterprises and government departments, there are certain management requirements for visitors, usually using traditional manual records or computer-aided manual management. These methods require certain personnel management costs, and they face underlying problems concerned with personal identification as well as security. In this paper, we analyze the functions and features of cloud-edge collaboration and discuss the edge intelligence technology in the cloud-edge collaboration environment. Then by combining the architecture of the intelligent visitor system, we apply AI cloud and edge computing to collaboratively solve critical issues faced by the visitor system, such as real-time and data authenticity. The intelligent visitor system employs a Rockchip RK3399 motherboard, ID card reader, microphone array, camera, and other hardware to build an edge computing environment. Combined with Baidu AI cloud, the system has an intelligent visitor system with face recognition and voice interaction capabilities, which can realize verification of visitor information, voice self-registration, and automatic measurement of visitors’ body temperature and other functions.

ANALYSIS OF INSECTICIDE SUSCEPTIBILITY DATA FOR DENGUE VECTORS USING INSECTICIDE RESISTANCE MANAGEMENT SYSTEM (IRMS)

Laboratory testing generates data, and data analysis contributes to the production of results that aid in problem resolution. As more samples are tested, more data is generated, and the complexity of data management increases. Surveillance monitoring and real-time assessment are crucial in managing insecticide resistance; consequently, manual management of insecticide resistance data requires digital transformation. The Insecticide Resistance Management System (IRMS) was developed in this study to analyse and manage 244 insecticide susceptibility data. This system permits immediate analysis following the entry of laboratory data. Red dots dominated green dots on a map displayed in IRMS depicting the prevalence of insecticide-resistant mosquito populations in both Aedes species. In addition, the heatmap revealed that the majority of Aedes aegypti were resistant to all tested insecticides during all monitoring cycles. This system circumvents the time-consuming limitations of manual data analysis by enabling rapid result visualisation and report generation.

Perancangan Sistem Informasi Pengelolaan Webinar Berbasis Web

Webinar activities that are carried out online allow a large number of participants from various origins, so manual management will be very troublesome for the organiser. The development of web-based applications allows developers to build applications quickly and easily. In the development stage of the web-based application, the first step that must be done is to create an application development design. The design of the application is based on the needs of the application manager which we can get from various ways such as studying related documents, interviews or literature studies on similar research. This research will conduct a needs analysis and apply it to the design of an application that will be used by webinar managers whose aim is to facilitate the process of managing webinar activities from the process of scheduling activities, registering participants to printing webinar certificates which can be done independently by the webinar participants.

Digital Anthropological photography

Despite the fact that in recent years the anthropologist's arsenal has significantly expanded due to the intro-duction of digital 3D scanning, computed tomography, microtomography, etc. into the practice of anthropological research, for most researchers photography remains an important part of the scientific process. Moreover, the resulting images are increasingly subject to higher requirements, since they often appear in scientific circulation much faster than before, bypassing editors and professional retouchers of publishers thanks to various kinds of Internet resources, such as presentations, on-line Internet conferences, reports, etc. In this new digital reality, the researcher acts as both an expert, a director, and an operator of a photo session and is solely responsible for the quality of the result and for its compliance with the goals of the shooting. The high intelligence of modern digital cameras creates a false impression in the beginner’s mind that camera can always be given freedom in making decisions regarding the shooting parameters. However, as shown in the article, there are a number of shooting situations when targeted manual management of shooting parameters is necessary to obtain a positive result. The following information will help the photographer do this. The purpose of our article is to help the researcher anthropologist qualitatively solve his problems using a digital camera. We will try to give an idea of those basic concepts, features of technology and techniques that determine the work of a photographer within the digital space. To this end, the article discusses the main technical and methodological techniques of anthropological photography within the digital space. A brief definition of the basic concepts of the “digital world” and the most important technical characteristics of modern digital cameras are given. The main part of the article is devoted to photography of paleoanthropological materials. Particular attention is paid to the shooting of the skull and odon-tological materials. Specific recommendations are given on the management of shooting parameters and on the organization of the shooting process, the use of which will allow the researcher to obtain high-quality digital pho-tographs of the studied anthropological objects that meet both the requirements of modern printing and the re-quirements of representation on Internet resources.

ACT-2 JCOG0911A2 AND ESTABLISHING AN INFRASTRUCTURE FOR RADIOLOGICAL STUDIES FOR JCOG BRAIN TUMOR STUDY GROUP CLINICAL TRIALS

Abstract Background Radiological information is indispensable to understanding the pathology and correctly assessing the treatment response of malignant brain tumors. Clinical trials conducted outside of Japan have vigorously collected and investigated radiological images of the enrolled patients and published many significant findings, such as the RANO criteria. The authors initiated the JCOG0911A2 study, which is an ancillary analysis of JCOG0911, and established an infrastructure for collecting and analyzing raw radiological images of patients enrolled in clinical trials conducted by the JCOG brain tumor study group. JCOG0911A2 study design and infrastructure for radiological image storage: JCOG0911A2 aims to collect all radiological images related to the JCOG0911 study, conduct radiomics analysis, and complete general radiological research. Among 122 registered patients, 2532 series from 118 cases accounting for 237 GB of data were collected as of June 28, 2022. All collected data were anonymized and stored in Osirix MD with triple data backup. JCOG0911A2's treatment response assessment will be performed according to the RANO criteria. Thus, the authors developed a script in MATLAB that enables semi-automatic treatment response assessment according to the investigators' annotations on Osirix MD. Data Management The abovementioned “semi-automatic treatment response assessment script” was not necessary until up to a collection of 30 cases. However, manual management became impossible as more case data arrived, and establishing a sophisticated and systematic data management system was inevitable. The data management system developed for JCOG0911A2 is also used with minor modifications for the ongoing JCOG study. Conclusion JCOG0911A2 study established a reliable infrastructure capable of handling radiological images from patients enrolled in JCOG brain tumor study group clinical trials.

The view on systems monitoring and its requirements from future Cloud-to-Thing applications and infrastructures

Monitoring infrastructures is a key cornerstone for both security and resilience mechanisms, as well as, infrastructure management helping to ensure availability and reliability. Its importance is increasing in the light of Internet-of-Things (IoT) infrastructures growing both in size and complexity, hence, widening the attack surface and decreasing the possibilities of manual management. There is a multitude of different monitoring concepts providing solutions to those problems. However, in the context of IoT those traditional concepts are limited in their adoption capabilities due to fundamental differences in architecture and structure. In this paper, we create a systematization of knowledge about the impact of IoT infrastructure characteristics on requirements for and architecture of monitoring. In particular, we first discuss definitions of monitoring resulting in an own definition, before elaborating on the terminology encompassing monitoring. We further discuss requirements imposed by IoT systems combined with an analysis of monitoring properties relevant for IoT. As part of this paper, we also focus on highlighting current and future directions of IoT architecture and infrastructure.

Zero Touch Networks to Realize Virtualization: Opportunities, Challenges, and Future Prospects

The heterogeneity and growing complexity from the evolution of 5G and beyond networks and the development of future IoT networks necessitate the automatic management of these networks. The exacerbated growth of IoT devices and foreseen network complexity deem manual management impractical. Zero touch networks (ZTNs) seems the practical solution to utilize the potential of virtualization by incorporating autonomous services and infrastructure to meet customer needs automatically. The ZTN is envisioned to achieve operation autonomy by enabling configuration, monitoring, optimization, and healing without human intervention. However, the openness of the ZTN framework, network heterogeneity and complexity, diverse and vertical industries, dynamicity, and so on complicate automation. It requires extensive research and development efforts to accomplish this goal. This study discusses the challenges of ZTNs to obtain the full potential of virtualization and highlights future trends in this regard.

A Decision Support System Model for Middle School Education Management Based on Sparse Clustering Algorithm

With the development of China’s economy, education has also been greatly improved, and the enrollment scale has been continuously expanded. It has become increasingly difficult to manage student performance. The traditional manual management of student grades has shortcomings such as low efficiency, poor privacy, difficulty to find and maintain, and incomplete knowledge mining of student grade data resources, so it is impossible to fully test and utilize related technologies. Based on this background, this paper designs and develops a set of decision support systems for middle school education management combined with a sparse clustering algorithm according to the actual needs of middle school education. The article first introduces the concept of sparse clustering algorithm and the four levels of the decision support system for middle school education management, data layer, model layer, application layer, and display layer, and then introduces based on five system models, namely, database design, online analysis process module design, data mining design, model library design, function design, and finally, the system model of educational decision management support is constructed, and the model test is completed. The practice shows that the system proposed in this paper can manage the current educational information of students scientifically, extract the information from the changes in students’ achievements in time, and make certain assistance for teachers’ educational decisions. This paper designs an effective decision support system by studying the sparse clustering algorithm and introducing it into the field of middle school education management.

Teaching Archives Information Management and Quality Diagnosis Method from the Perspective of Collaborative Education Based on Intelligent Computing

With the growth of better education, improving the quality of better education has become a top priority. In order to facilitate the sustainable and healthy growth of better education and continuously improve the quality of education for faculty and staff, the Ministry of Education has established a regular assessment system for undergraduate-level education. This article analyzes the quality diagnosis of teaching archives management from the perspective of collaborative education by using the bionic method and summarizes the three methods. In the precision rate, the user interface is the highest, followed by accuracy, security requirements, hardware environment, system maintenance, and information flow; in the recall rate, the security requirements are the highest, followed by the hardware environment, accuracy, user interface, information flow, and system maintenance; in F1, the user interface is the highest, followed by hardware environment, information flow, accuracy, security requirements, and system maintenance. In the particle swarm optimization coverage algorithm, the security requirements are obtained, the user interface accuracy is relatively high, and the hardware environment is relatively low; the system maintenance and hardware environment are the most covered, and the security requirements are the least; the number of rejected samples is the largest. It is the hardware environment and system maintenance, and the least is the security requirement; the most correct number of rejected samples is the hardware environment and system maintenance. The scientific analysis of the quality diagnosis of teaching archives management from the view of collaborative education is extremely important for improvement of teaching. The archives management system can be effective in the process of the archives hardware environment, security requirements, system maintenance, user interface, information flow, and accuracy. The efficiency of the manual management process is improved, and the process is simplified.

Machine Learning Tools and Platforms in Clinical Trial Outputs to Support Evidence-Based Health Informatics: A Rapid Review of the Literature

Background: The application of machine learning (ML) tools (MLTs) to support clinical trials outputs in evidence-based health informatics can be an effective, useful, feasible, and acceptable way to advance medical research and provide precision medicine. Methods: In this study, the author used the rapid review approach and snowballing methods. The review was conducted in the following databases: PubMed, Scopus, COCHRANE LIBRARY, clinicaltrials.gov, Semantic Scholar, and the first six pages of Google Scholar from the 10 July–15 August 2022 period. Results: Here, 49 articles met the required criteria and were included in this review. Accordingly, 32 MLTs and platforms were identified in this study that applied the automatic extraction of knowledge from clinical trial outputs. Specifically, the initial use of automated tools resulted in modest to satisfactory time savings compared with the manual management. In addition, the evaluation of performance, functionality, usability, user interface, and system requirements also yielded positive results. Moreover, the evaluation of some tools in terms of acceptance, feasibility, precision, accuracy, efficiency, efficacy, and reliability was also positive. Conclusions: In summary, design based on the application of clinical trial results in ML is a promising approach to apply more reliable solutions. Future studies are needed to propose common standards for the assessment of MLTs and to clinically validate the performance in specific healthcare and technical domains.

PipeConf: An Integrated Architecture for the Automated Configuration of Network Assets

The manual management of network assets is susceptible to configuration errors, lack of standardization, a large amount of repetitive work, and little or no traceability of changes over time. The Infrastructure as Code (IaC) approach makes it possible to automate the process of configuring resources such as operating systems, network services, containers, and applications, by treating them as software and allowing standardization and configuration rollback. This paper proposes an integrated architecture based on different software tools that use the IaC approach to automate the configuration of network assets, considering different models and manufacturers. A quantitative analysis shows the architecture’s efficiency in response time and scalability. The architecture has achieved a proportional gain of 83% in the average response time to manage 128 assets, with no significant increase in processing and memory usage.

Design and Implementation of Vocational Education Management System Based on Discrete Fourier Transform

In some remote areas and even some vocational colleges, the information management of students is still stagnant on paper. Such traditional methods have obviously lagged behind the development and needs of society. The traditional manual management is a waste of both manpower and material resources. In today’s rapidly developing information age, it is very necessary to study an efficient vocational education management system. The vocational education management system designed in this paper includes Web server, DB server, college management terminal, and load balancing switch. In this system, CONN.ASP is a common module of all ASP programs in the whole system. It is mainly used to realize the connection between ASP program and database and the calling of common function submodules. In order to reduce the power consumption of the vocational education management system, the Discrete Fourier Transform (DFT) theory is proposed in this paper. This algorithm can effectively save the size of the storage unit and the number and frequency of reading the twiddle factor, so as to achieve the purpose of improving the operation speed. This paper randomly selected the scores of 10 classes and use the system for data processing test. A total of 50% of teachers were particularly satisfied with the tables generated by the discrete Fourier transform-based vocational education management system. They believed that the vocational education management system based on discrete Fourier transform is very fast in data processing, and the processing speed was within 0.1 s. The system designed in this paper can meet various demands in vocational education management and continuously enhance the function of educational administration management.

The Laboratory Intelligent Management System Using NB-IoT and Artificial Intelligence Technology

For the traditional laboratory manual management, there is a need for laboratory managers. The managers cannot keep abreast of the laboratory conditions, and forget to shut down and close the windows, causing waste of electricity and equipment damage. Based on NB-IoT and artificial intelligence, a laser intelligent management system is developed using technologies such as network communication, intranet, automatic control, artificial intelligence, and software. Through this system, administrators can grasp the situation of the laboratory in real time, realize the intelligent management of the laboratory, improve the scientific management of the laboratory, improve the work efficiency of laboratory technicians, and better play the laboratory as an important platform for scientific research training in colleges and universities. This paper aims to develop an intelligent laboratory management system based on NB-IoT and AI technology. Through this system, the manager can grasp the situation of the laboratory in real time, realize the intelligent management of the laboratory, and better play the role of the laboratory as an important platform for scientific research and talent training.

Physical Education Resource Information Management System Based on Big Data Artificial Intelligence

With the rapid development of artificial intelligence technology, the combination of physical education and modern technology has become the development trend of today’s physical education. Especially in the context of the reform of the education system, the connection between artificial intelligence and physical education is getting closer and closer and it has penetrated into all aspects of physical education. In terms of sports management, the sports management method based on big data artificial intelligence has gradually replaced the traditional manual management method and promoted the development of physical education. Strengthening the information management of physical education teaching and providing high-quality and efficient physical education information for physical education managers and teachers are an important part of the current school physical education teaching work. The establishment of sports resource information big data platform is the key to the integration and sharing of sports resources. This paper discusses the relevant theories of artificial intelligence and studies the current status of the application of sports resources. A physical education resource information management system based on artificial intelligence is constructed. The feasibility of the system is verified by the number of system visitors and the distribution of user populations, and the real-time information, effectiveness, students’ learning initiative, and teachers’ teaching initiative of this system and the original system are compared and analyzed. The results show that the average score of the real-time information of the original system is 74.28 points, the average score of effective information is 82.29 points, the average score of students’ learning initiative is 74.97 points, and the average score of teachers’ teaching initiative is 82.98 points. The average score of real-time information in the new system is 92.05 points, the average score of information effectiveness is 93.34 points, the average score of students’ learning initiative is 82.69 points, and the average score of teachers’ teaching initiative is 90.46 points. The real-time information and information effectiveness scores of the new system are higher than those of the original system, and the scores of students’ learning initiative and teachers’ teaching initiative are higher than those of the original system.

Efficacy of Isoproturon and Pendimethalin against Resistant Biotypes of spp. in Wheat ( L.)

Persistence of weeds under irrigation conditions is inevitable and manual management practices are labour intensive and cumbersome. As far as wheat is concerned huge losses in yield, are incurred due to spp. Therefore, the present study was carried out Rumex to evaluate the efficacy of herbicides against spp. during the 2017-18 at Department of Agronomy, Chaudhary Charan Singh Rumex Rabi, Haryana Agricultural University, Hisar (Haryana). Isoproturon as post- emergence and pendimethalin as pre-emergence were taken as treatments and applied at three doses (0.5X, 1.0X and 2.0X) under pot study 'X' is the recommended dose of herbicide@ 1000g/ha for isoproturon and 1500g/ha for pendimethalin .Four populations of spp. named as HHH (HAU Hisar), UPH (Ujha, Panipat), JHH (Jind) Rumex and JJR (Jhajjar) collected from putative resistance affected farmer's field.UPH and JHH populations are highly resistant whereas JJR population is moderately resistant to sulphonylureas herbicides. HHH population is sensitive so used as standard check for comparison. Majority of biotypes showed sensitivity against isoproturon at recommended dose except UPH biotype. It provided 70-90 per cent control to all biotypes at double of recommended doses. Lower values of plant height, chlorophyll fluorescence, fresh and dry weight were observed in isoproturon treated plants. biotypes were highly sensitive to pendimethalin when applied as pre emergence. It provided complete Rumex control to all biotypes. The information collected from this study will facilitate proactive management of spp. through sequential Rumexapplication of pre and post emergence herbicides.

Farmer Friendly Agriculture Robot with Shovel Control System Using Automatic Seed Dispensing

Abstract: An agricultural mechanism or agri mechanism could be a mechanism deployed for agricultural functions. The most space of application of robots in agriculture is seeding robots square measure designed to exchange human labor. The agricultural business is behind alternative complementary industries in victimization robots as a result of the kind of jobs concerned in agriculture are not any and dig. This project strives to develop a mechanism capable of activity operations like automatic seeding and dig. It additionally provides manual management once needed and keeps tabs on the wetness with the assistance of wetness sensors Then main part here is that the microcontroller that supervises the whole method.

Rancang Bangun Sistem Informasi Pengarsipan dan Pendistribusian Surat

Archive management is very important for an institution. Archive management at Panca Marga University has not been done computerized. Manual management raises several problems, including slow search, need for extensive mail storage and rather slow distribution. These problems can be solved by building an information system for filing and distributing mail. System development use the waterfall method. In this study, a system can be built to be able to manage incoming letters, outgoing letters, decision letters, letter dispositions along with the distribution of the letters. Based on the results of black box testing, the system that was built was running according to its functionality.

Implementation of a Meeting Sign in Method Through Image Recognition

This system utilizes image recognition technology to study the problems of other people's check-in in the current check-in mode, poor data feedback, and low efficiency of manual management. A complete set of mobile conference sign-based based on image recognition technology is designed and implemented. The management system can greatly improve the efficiency of the conference check-in, save the time of the conference check-in, has strong usability and operability, and greatly saves the conference time occupied by the conference sign-on, and has high efficiency and practicability.

Knowledge-Powered Explainable Artificial Intelligence for Network Automation toward 6G

Communication networks are becoming increasingly complex toward 6G. Manual management is no longer an option for network operators. Network automation has been widely discussed in the networking community, and it is a sensible means to manage the complex communication network. Deep learning models developed to enable network automation for given operation practices have the limitations of lack of explainability and inapplicability across different networks and/or network settings. To tackle the above issues, in this article we propose a new knowledge-powered framework that provides a human-understandable explainable artificial intelligence (XAI) agent for network automation. A case study of path selection is developed to demonstrate the feasibility of the proposed framework. Research on network automation is still in its infancy. Therefore, at the end of this article, we provide a list of challenges and open issues that can guide further research in this important area.

A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm.

The disposal of municipal solid waste (MSW) is based on the divide-regional operation model at present, which brings management convenience. However, due to the constraints of the division between the operating regions, there are problems such as inflexible vehicle scheduling and low efficiency. Changing this mode will put pressure on management. It is necessary to break the constraint of the region and form a fully automatic operating scheme without too much manual management. First, the initial clusters are formed by considering the distance in the initial allocation module. Secondly, through the type labeling and reallocation module, the single big data set is transformed into multiple small data sets by considering the allocated amount of garbage and the carrying capacity of garbage vehicles. Then, this work proposes the improved hierarchical agglomerative clustering (IHAC) algorithm and the garbage collecting path planning (GCPP) algorithm to realize the intelligent allocation of waste and scheduling route planning of garbage vehicles. Finally, through simulation by real example and comparative analysis, the advantages of the proposed scheme are discussed. The results show that the proposed scheme is more effective than the original scheme and other advanced methods, which can provide decision support for the scientific and intelligent collection and transportation of MSW.