Autonomous Environment Research Articles

Effectiveness of School Management through Strengthening the Managerial Ability of School Principals in the Field of Education Financing

Along with the demands of an autonomous environment, it has an impact on the condition of schools that are increasingly competitive. Good management of education costs optimizes school functions to be more efficient and effective so as to improve school performance, quality and competitiveness. The results of the author's observations by observing several complaints from educators in designing activity programs to improve the quality of education require critical educational activities for educators and principals about education finance management. By using the workshop method, it is hoped that the objectives of this PKM activity are (1) providing knowledge to educators in preparing programs according to the education budget and (2) Providing skills in managing education budgets. This activity is designed offline whenever possible in the Meeting Hall at SDN 004 Bontang City and online if participants from outside Bontang City are not possible (during the COVID-19 Pandemic) to be carried out using the Zoom application. The methods used are explanatory methods, demonstrations and practices by means of seeding and questioning. The implementation of activities is carried out by providing material on strategic management of program preparation. The expected result in this training is that all skilled educators develop a program of activities with the preparation of the right budget

Autonomous environment perception and obstacle avoidancetechnologies of Zhurong Mars rover

<p indent="0mm">Mars surface exploration has the characteristics and constraints of harsh and complex environment, lack of prior knowledge, and strict limitation of computing resources. Autonomous environment perception and obstacle avoidance technology is the key to the ability of autonomous exploration, and also one of the key technologies in the field of deep space exploration at home and abroad. In China Tianwen-1 Mars exploration mission, the Zhurong Mars rover, China’s first Mars surface mobile exploration, has realized efficient autonomous environmental perception, obstacle avoidance planning and control under the constraints of limited resources, significantly improved the efficiency of mobile exploration, and the average moving speed is nearly 5 times higher than that of Yutu-2 lunar rover. This paper briefly introduces the sensor configuration, movement mode function and process of Zhurong Mars rover supporting autonomous perception and obstacle avoidance movement, and focuses on the breakthroughs in key technologies of efficient perception and obstacle avoidance planning, including binocular stereo vision efficient perception method based on fast parallel processing, in order to realize the efficiency and safety of autonomous perception and obstacle avoidance movement under severe resource constraints Based on the environmental modeling and obstacle avoidance planning method of trafficability analysis. Finally, the autonomous perception and obstacle avoidance mobile detection of Zhurong Mars rover during the mission is summarized, and the technical development thoughts on further enhancing the autonomous perception and obstacle avoidance ability of Mars rover in the future are put forward in the end.

Research on Geometric Design Standards for Freeways under a Fully Autonomous Driving Environment

The formulation of the current geometric design standard for freeways considers the influence of the “human-vehicles-road-environment” system. In the fully autonomous environment, the driver has been liberated from the system, which means that the influence of “human” will be reduced. The requirements or limitations of the freeway geometric design standard, which consider the driver’s psychological and physical factors in the traditional driving environment, can be more flexible in the fully automatic driving environment. By using the methods of comparative analysis, theoretical analysis, and theoretical calculation, this article researches the geometric design standard for freeways in an autonomous driving environment from four aspects: control elements of geometric design, horizontal alignment, vertical alignment, and cross-section elements. The main contribution of this article is to propose the recommended values of the geometric design standard for freeways in a fully autonomous driving environment, which can provide reference for the formulation of relevant standards or specifications for autonomous driving roads in the future.

Preparation of Learners’ Autonomous Learning and Their Views on Learning Content Selection in Autonomous Classroom Environment

Preparation of Learners’ Autonomous Learning and Their Views on Learning Content Selection in Autonomous Classroom Environment

Factors affecting academic staff development in the context of university autonomy through the lens of stakeholders: a case study from Tay Nguyen University, Vietnam

PurposeThis paper aims at identifying perceived factors and measuring opinions about the factors' impact on academic staff development (ASD) at Tay Nguyen University (TNU), Dak Lak, Vietnam.Design/methodology/approachThe research used the exploratory factor analysis (EFA) method, the multiple regression model (ordinary least squares (OLS)) and a five-point Likert scale questionnaire. A sample of 70 managerial staff, 374 lecturers and 512 students of TNU was surveyed to obtain data.FindingsThe EFA showed that opinions concerning university autonomy (UA) and university social responsibility (USR) were positively correlated. With the above two factors united as responsible autonomy (RA), the OLS indicated perceptions that RA and internal driving factor (IF) had significant and positive impacts on the ASD, while external driving factor (EF) was found to have a perceived negative influence on ASD.Research limitations/implicationsThe results indicated that there appears to be a close relationship between UA and USR, and these can be considered as a factor that has apparent impacts on the ASD of the university.Practical implicationsThe degree of UA and USR of TNU should be enhanced through awareness of the university's academic staff, the application of a suitable evaluation system and the efficiency of university's regulations. In addition, efforts should be made to improve internal factors such as the dissemination of educational philosophy, the suitability of strategic plans, the development of key performance indicators KPIs and the building of organizational culture – all of which will help to heighten the university’s ASD. At the same time, TNU should endeavor to quickly transform aspects of administration and management to meet the shifting requirements of the autonomous environment and competitive features of the market economy. In particular, there is a need for the academic staff themselves to have increased capacity to adapt to these changes.Social implicationsThe authors' results have a broader application to not only the case of TNU but to other situations in developing countries where universities are in transitional stages as governments assign increasing autonomy and responsibility to them.Originality/valueThis paper suggests that the academic staff of TNU should be provided with both professional autonomy and adaptive capacity to foster research and educational innovation in the market-based higher education system of Vietnam. More generally, if true, the paper suggests that an increase in the degree of UA and USR should be combined with the efforts to improve the internal environments such as disseminating educational philosophy, mission, vision and strategies and building organizational culture.

A Global Max-Flow-Based Multi-Resolution Next-Best-View Method for Reconstruction of 3D Unknown Objects

Many robot tasks, such as grasping and inspection, may require complete 3D models with enough surface details. Fully autonomous environment exploration and 3D reconstruction of unknown objects are challenging for a robot when little or no knowledge about an object is known as a priori. Previous work updated sensor measurements from a greedy ordered set of views into a map with an unchangeable voxel size, leading to the lack of details on the object surface, sampling problems, and no adaption to small objects. We propose a global max-flow-based multi-resolution next-best-view (NBV) method to improve performance on these problems. In particular, it utilizes a max-flow-based global view quality function to obtain optimal NBVs, and a multi-resolution strategy to optimize the reconstruction quality and efficiency. Results of comparative simulation experiments with state-of-the-art (SOTA) methods show that our method achieves a higher voxel coverage under the same resolution. Parametric and ablation studies confirm that the global view function and multi-resolution strategy are effective. Results of real-world experiments show that our method achieves complete reconstruction of small objects with only 6–8 views.

Changing Role of College Librarians’ in Autonomous Environment: A Study

According to University Grants Commission (UGC) guidelines for autonomy, the only way to improve quality in higher education is to assign academic freedom to colleges affiliated to the university. Quality education is the prime motto of the Indian government and to ensure quality in higher education government of India is endorsing academic autonomy from the last few years. To assess the influences on the academic libraries of an autonomous environment and to examine whether there is any change in the role of academic librarians the researcher opted for the present study. Twenty-five autonomous college librarians from Mumbai were selected as the population for the study. The Role Efficacy scale of Udai Pareek was used to analyze the changing role of librarians with telephone interview. The present study throws some light on the changing role of librarians in the autonomous environment.

Autonomous Environment Disinfection Based on Dynamic UV-C Irradiation Map.

The COVID-19 pandemic has become a worldwide concern and has motivated the entire scientific community to join efforts to fight it. Studies have shown that SARS-CoV-2 remains viable onsurfaces for days, increasing the chances of human infection. Environmental disinfection is thus an important action to prevent the transmission of the virus. Despite the valuable contribution of the research community to the field of UV-C disinfection by robots, there still lacks a disinfection system that is fully autonomous and computes its trajectory in real-time and in unknown environments. To meet this need, we propose an autonomous UV-C disinfection strategy for indoor environments based on a dynamic Irradiation Map that indicates the amount of energy applied in each region. Our method was tested in different scenarios and compared with other disinfection strategies. Experiments show that our approach delivers better results, especially when targeting high ideal UV-C doses.

Deep Learning-Based Object Detection and Scene Perception under Bad Weather Conditions

Large cities’ expanding populations are causing traffic congestion. The maintenance of the city’s road network necessitates ongoing monitoring, growth, and modernization. An intelligent vehicle detection solution is necessary to address road traffic concerns with the advancement of automatic cars. The identification and tracking vehicles on roads and highways are part of intelligent traffic monitoring while driving. In this paper, we have presented how You Only Look Once (YOLO) v5 model may be used to identify cars, traffic lights, and pedestrians in various weather situations, allowing for real-time identification in a typical vehicular environment. In an ordinary or autonomous environment, object detection may be affected by bad weather conditions. Bad weather may make driving dangerous in various ways, whether due to freezing roadways or the illusion of low fog. In this study, we used YOLOv5 model to recognize objects from street-level recordings for rainy and regular weather scenarios on 11 distinct classes of vehicles (car, truck, bike), pedestrians, and traffic signals (red, green, yellow). We utilized freely available Roboflow datasets to train the proposed system. Furthermore, we used real video sequences of road traffic to evaluate the proposed system’s performance. The study results revealed that the suggested approach could recognize cars, trucks, and other roadside items in various circumstances with acceptable results.

Investigating reading motivation in Latinx college students: qualitative insights from bilingual readers

ABSTRACT The existing literature on reading motivation primarily focuses on monolingual children and adolescents using quantitative approaches. To date, there is an absence of qualitative studies exploring bilingual college students’ reading motivation that can provide details of participants’ views, beliefs, and experiences related to reading motivation. To meet this need, this qualitative study used the bioecological model of human development to explore sociocultural impacts on multidimensions of reading motivation in Latinx college students. Twenty-eight Latinx bilingual college students participated in individual and semi-structured interviews and generated written literacy journeys that focused on understanding factors affecting the multidimensionality of their reading motivation and ways to motivate them. Our data suggest Latinx college students’ reading motivation was influenced by the three systems of the bioecological model of human development: microsystem, exosystem, and macrosystem. Furthermore, we found that reflective, autonomous, and interactive learning environments were effective for enhancing their reading motivation.

Teaching English as a Foreign Language in the Post-COVID Era – Lessons from the Pandemic for Agile, Autonomous and Hybrid Learning Environments

Teaching English as a Foreign Language in the Post-COVID Era – Lessons from the Pandemic for Agile, Autonomous and Hybrid Learning Environments

Efficient and Safe Robotic Autonomous Environment Exploration Using Integrated Frontier Detection and Multiple Path Evaluation

Autonomous exploration and remote sensing using robots have gained increasing attention in recent years and aims to maximize information collection regarding the external world without human intervention. However, incomplete frontier detection, an inability to eliminate inefficient frontiers, and incomplete evaluation limit further improvements in autonomous exploration efficiency. This article provides a systematic solution for ground mobile robot exploration with high efficiency. Firstly, an integrated frontier detection and maintenance method is proposed, which incrementally discovers potential frontiers and achieves incremental maintenance of the safe and informative frontiers by updating the distance map locally. Secondly, we propose a novel multiple paths planning method to generate multiple paths from the robot position to the unexplored frontiers. Then, we use the proposed utility function to select the optimal path and improve its smoothness using an iterative optimization strategy. Ultimately, the model predictive control (MPC) method is applied to track the smooth path. Simulation experiments on typical environments demonstrate that compared with the benchmark methods, the proposed method reduce the path length by 27.07% and the exploration time by 27.09% on average. The real-world experimental results also reveal that our proposed method can achieve complete mapping with fewer repetitive paths.

Multi-Agent Autonomous Cleaning

Abstract: In today’s world, robots are taking over the world by doing the tasks which used to be done by humans a while ago. Robots are continuously evolving into better and more efficient autonomous agents, makes substantial growth in fields like adaptive artificial intelligence. Our main objective of this people is to create an efficient multi agent autonomous environment for robots for cleaning purposes. Keywords: Gradient Descent, Centralized controller, autonomous agents, LiDAR

Orchestration of autonomous trusted third-party banking

Background Digital transformation is changing the structure and landscape of future banking needs with much emphasis on value creation. Autonomous banking solutions must incorporate on-the-fly processing for risky transactions to create this value. In an autonomous environment, access control with role and trust delegation has been said to be highly relevant. The aim of this research is to provide an end to end working solution that will enable autonomous transaction and task processing for banking. Method We illustrate the use case for task delegation with the aid of risk graphs, risk bands and finite state machines. This paper also highlights a step by step task delegation process using a risk ordering relation methodology that can be embedded into smart contracts. Results Task delegation with risk ordering relation is illustrated with six process owners that share immutable ledgers. Task delegation properties using Multi Agent Systems (MAS) is used to eliminate barriers for autonomous transaction processing. Secondly, the application of risk graph and risk ordering relation with reference to delegation of tasks is a novel approach that is nonexistent in RBAC. Conclusion The novelty of this study is the logic for task delegation and task policies for autonomous execution on autonomous banking platforms akin to the idea of federated ID (Liberty Alliance).

Orchestration of autonomous trusted third-party banking.

Background Digital transformation is changing the structure and landscape of future banking needs with much emphasis on value creation. Autonomous banking solutions must incorporate on-the-fly processing for risky transactions to create this value. In an autonomous environment, access control with role and trust delegation has been said to be highly relevant. The aim of this research is to provide an end to end working solution that will enable autonomous transaction and task processing for banking. Method We illustrate the use case for task delegation with the aid of risk graphs, risk bands and finite state machines. This paper also highlights a step by step task delegation process using a risk ordering relation methodology that can be embedded into smart contracts. Results Task delegation with risk ordering relation is illustrated with six process owners that share immutable ledgers. Task delegation properties using Multi Agent Systems (MAS) is used to eliminate barriers for autonomous transaction processing. Secondly, the application of risk graph and risk ordering relation with reference to delegation of tasks is a novel approach that is nonexistent in RBAC. Conclusion The novelty of this study is the logic for task delegation and task policies for autonomous execution on autonomous banking platforms akin to the idea of federated ID (Liberty Alliance).

Pitch‐Black Nanostructured Copper Oxide as an Alternative to Carbon Black for Autonomous Environments

Nanostructured Copper Oxide In article number 2100049, a novel nanostructured copper oxide as an alternative to carbon black is demonstrated by Songtao Wu, Gunugunuri Reddy, Debasish Banerjee. This nanostructured copper oxide has specific prevalence of crystal facets, creating visually pitch-black material which yet selectively reflects near infra-red LiDAR detectable wavelength. This work opens new opportunities to use dark colored objects in future autonomous environment.

Autonomic cloud computing based management and security solutions: State‐of‐the‐art, challenges, and opportunities

AbstractThe advancements and rapid adoption of service oriented architecture, utility computing, virtualization, etc., have emerged in a new computing paradigm called cloud computing, in the last few years. Cloud computing enables on‐demand delivery of data, software, and hardware in the form of services over the Internet. But the growing scale of cloud‐users and type‐of‐services urge for an autonomous environment to automate the highly complex cloud environment in a suitable way to improve the overall management of the cloud systems. To counter these automation related issues, autonomic computing (AC) emerges as a potential solution to optimize the performance of cloud computing. The merge of these two prominent technologies gives rise to a new technological domain called autonomic cloud computing (ACC). In this survey article, the existing AC assisted solutions for cloud computing is broadly categorized into two groups, namely features based and parameters based. Features based solutions are further classified into management (of services, workload, and resources) and security aware solutions. Similarly, parameters based solutions are grouped into quality‐of‐service aware and performance management solutions. Finally, the comparative analysis of state‐of‐the‐art ACC approaches with respect to the approach referred, performance metrics, outcome, and the derived inference is also presented to have an in‐depth understanding of the existing literature. Overall, the work excites the research community to design the effective autonomous solutions for ACC resources and services. Additionally, several research gaps and challenges are identified and discussed in this article based on the study with a focus on the future research directions.

Pitch‐Black Nanostructured Copper Oxide as an Alternative to Carbon Black for Autonomous Environments

Light detection and ranging (LiDAR) systems are becoming crucial for measuring the distance and creating a point cloud of the local environment, critical data for artificial intelligence to enable collision‐avoidance mechanisms. However, LiDAR utilizes radiation in the near‐infrared (NIR) region of the electromagnetic spectrum, which is prone to complete absorption by typical dark (such as painted by carbon black) colored objects, leading to loss of timely data points. Till date a very limited number of solutions have been put forward to address this. Herein, nanocrystallites of copper (II) oxide with specific prevalence of crystal facets that create nearly perfect black material at visible wavelengths are proposed. The sharp transition of absorbance near 700 nm wavelength light is attributed to the near‐unity ratio of (−111)/(111) the crystal facets and a crystal size of around 100 Å for the (−111) plane. Although indistinguishable from carbon black and with the same degree of measured blackness (My value 135.5), the nanocrystalline CuO shows 1500% better detectability by LiDAR. The study paves the way for the unconstrained use of dark objects in future society and infrastructure, moving a step closer toward fully autonomous operation of vehicles and robots.

Survey of simultaneous localization and mapping based on environmental semantic information

The simultaneous localization and mapping (SLAM) technique is an important research direction in robotics. Although the traditional SLAM has reached a high level of real-time performance, major shortcomings still remain in its positioning accuracy and robustness. Using traditional SLAM, a geometric environment map can be constructed that can satisfy the pose estimation of robots. However, the interactive performance of this map is insufficient to support a robot in completing self-navigation and obstacle avoidance. One popular practical application of SLAM is to add semantic information by combining deep learning methods with SLAM. Systems that introduce environmental semantic information belong to semantic SLAM systems. Introduction of semantic information is of great significance for improving the positioning performance of a robot, optimizing the robustness of the robot system, and improving the scene-understanding ability of the robot. Semantic information improves recognition accuracy in complex scenes, which brings more optimization conditions for an odometer, pose estimation, and loop detection, etc. Therefore, positioning accuracy and robustness is improved. Moreover, semantic information aids in the promotion of data association from the traditional pixel level to the object level so that the perceived geometric environmental information can be assigned with semantic tags to obtain a high-level semantic map. This then aids a robot in understanding an autonomous environment and human–computer interaction. This paper summarized the latest researches that apply semantic information to SLAM. The prominent achievements of semantics combined with the traditional visual SLAM of localization and mapping were also discussed. In addition, the semantic SLAM was compared with the traditional SLAM in detail. Finally, future research topics of advanced semantic SLAM were explored. This study aims to serve as a guide for future researchers in applying semantic information to tackle localization and mapping problems.

Новое в профессиональных компетенциях персонала стационарного отделения скорой медицинской помощи

The purpose of the study is to determine the professional competencies that are in demand among emergency medical personnel when they provide medical care in an inpatient department. Materials and research methods. We compared the impersonal reports on the work done by 20 doctors of specialized substations of the emergency medical service in St. Petersburg for 2015–2017 with the information about work in the mode of daily activities in 2017 of 10 doctors in the intensive care unit of the inpatient department of the Emergency Medical Service of the First St. Petersburg State Medical University named after academician I.I. Pavlov, stored in the qMS medical information system of the University. The volume of instrumental examination of patients who sought medical help, received it in the required volume and were discharged in a satisfactory condition within the first day, was assessed. We compared the results of ultrasound diagnostics performed by the emergency doctors in the screening mode using portable equipment with the results of the work of a consultant-specialist. 701 medical records of inpatients from among those admitted to the Medical University were analyzed. By studying 4573 medical records, possibility and effectiveness of independent primary triage of the incoming patients by the nursing staff was determined. In addition, with the help of the FlexSim HealthCare program, which allows to effectively predict and to simulate changes in work processes within medical institutions, computer simulation of the department’s work was carried out in the conditions of autonomous work of an emergency doctor. Statistical comparison was performed using the Mann-Whitney test, Student’s t-test for unrelated values, and analysis of four-field tables using the χ-square test. Research results and their analysis. Analysis of the research results showed: fundamental principles of work of the staff of inpatient department — multidisciplinarity, multitasking, autonomy. An ambulance doctor working in a stationary environment must be able to independently diagnose, to treat patients with various pathologies, and to simultaneously supervise several patients; having the level of practical and theoretical training specified in the professional standard, the emergency doctor can effectively provide resuscitation care in the volume of I – II levels, which is especially important, given the short time spent by patients in the department; professional standard of the emergency doctors requires to master ultrasound diagnostics to identify gross pathological conditions and to perform a number of manipulations; nursing staff working in the inpatient department must be able to work in an autonomous environment. Triage by nurses is effective and allows to identify patients who need an immediate examination by an EMS doctor. The use of a three-level sorting algorithm does not contradict basic principles of medical triage used in world practice.