Data Processing Skills Research Articles

An assessment of whether educated non-researcher audiences understand how to reuse research data

PurposeThe purpose of this study is to assess whether educated non-researcher audiences understand how to reuse research data stored in a data repository.Design/methodology/approachA total of 44 participants in two user studies were asked to study a data set accessed from re3data.org. The participants were non-researcher audiences of the disciplines of the selected data sets. They were asked to figure out whether they understood how to reuse a data set after reading all the metadata or contextual information about the data set.FindingsMost participants reported that they figured out how to reuse the data, although their self-reports can be an overestimated assessment. However, the participants understand how to reuse a data set either numerically or statistically significantly worse than what the data set is, how it was collected or created and its purpose. Data set type tends to play a role in understanding how to reuse data sets and the purpose of data sets. Participants reported that unless a data set is self-explanatory, instructions on data set reuse and the purpose of data set were necessary for understanding how to reuse data set. However, because data reuse requires domain knowledge and data processing skills, some non-researcher audiences who lack domain knowledge and data processing skills may not understand how to reuse the data set in any way.Research limitations/implicationsThis study’s findings enrich the theoretical framework of data sharing and reuse by expanding the necessary information to be included in data documentation to support non-researchers’ data reuse. The findings of the study complement previous literature.Practical implicationsThis study extended previous literature by suggesting detailed data reuse instructions be included in data documentation if data producers and data curators wish to support educated non-researchers’ data reuse. This study’s findings enable policymakers of research data management (RDM) to formulate guidelines for supporting non-researchers’ data reuse. If data curators need to work with data producers to prepare the instructions on data reuse for non-researcher audiences, they probably need computing and data processing skills. This has implications for Library and Information Science schools to educate data librarians.Originality/valueThe research question is original because non-researcher audiences in the context of RDM have not been studied before. This study extended previous literature by suggesting detailed data reuse instructions be included in data documentation if data curators and data producers and data curators wish to support educated non-researchers’ data reuse. This study’s findings enable policymakers of RDM to formulate guidelines for supporting non-researchers’ data reuse.

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

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

Improving Understanding of Data Processing Concepts through the TARL Approach (Teaching at the Right Level) in Class 5 Students of SD Muhammadiyah 23 Surakarta

This study aims to enhance the understanding of data processing concepts among fifth-grade students at SD Muhammadiyah 23 Surakarta through the Teaching at the Right Level (TaRL) approach. The research was conducted using a Classroom Action Research (CAR) methodology, which involved two cycles, each consisting of two meetings. The primary issue identified was the low level of students' comprehension of data processing concepts, such as data collection, classification, interpretation, and the use of diagrams and tables. To address this, an active and contextual learning approach was implemented, involving real-life data collection and analysis, the use of digital tools for data visualization, and Problem-Based Learning (PBL) strategies.In the first cycle, the intervention revealed limited improvement in student engagement and learning outcomes. Consequently, the second cycle introduced revised group formations and additional instructional strategies, including ice-breaking activities. The results showed a significant improvement in student participation and understanding, with an increase in average class scores from 65 in the first cycle to 81 in the second cycle. The percentage of students meeting the learning criteria rose from 14.28% to 82.14%. This study concludes that applying TaRL, along with improved group dynamics and interactive learning methods, effectively enhances students' data processing skills and overall learning outcomes.

Application of Artificial Intelligence-based Technology in College Archives Management

This study looks into the use of artificial intelligence (AI)-based technologies, notably Convolutional Neural Network (CNN) models and data augmentation approaches, in college archives management. With the rapid expansion of digital information, archive organizations have enormous issues in maintaining, organizing, and giving access to their vast collections of historical documents and scholarly resources. AI solutions present possible solutions to these difficulties by automating labor-intensive operations and improving the efficiency and accuracy of preservation workflows. In this study, they investigate the transformational potential of CNN models in digitization activities, utilizing their visual data processing skills to automate and enhance the digitization of visual resources within college archives. In addition, they look into how data augmentation techniques might improve the resilience and generalization capabilities of AI models, reducing biases and improving performance across a variety of archive collections. Using empirical research, theoretical analysis, and case studies, they present insights into the opportunities and obstacles of implementing AI-based technology in college archive management. Furthermore, they discuss ethical implications for the deployment of AI solutions, emphasizing the significance of transparency, accountability, and stakeholder participation. This study aims to inform archival practitioners, technologists, and stakeholders about the effective and responsible integration of AI technologies in cultural heritage preservation and promotion within academic institutions by elucidating the practical applications and implications of CNN models and data augmentation techniques in archival contexts.

Formation of digital competences of older teenagers in the process of sports and recreational activities

The educational process, among other important tasks, should be directed to the formation of digital competences of children and school youth, including in the process of sports and health activities, since information technologies open up wide opportunities for the introduction of new modern forms and methods in working with children and school youth.
 The formation of digital competences of older teenagers in the process of sports and health activities is an integral part of the process of forming a mature personality, prepared for life, ready for self-analysis and self-realization, able to resist the unwanted effects of information flow and manipulation.
 A methodological toolkit aimed at forming the digital competences of older teenagers in the process of sports and recreational activities can be: cloud services in education, Web 2.0 services, creation of presentations; conducting video lessons, online training; development of online travel; involvement of older teenagers in research and information projects aimed at popularizing a healthy lifestyle, expanding knowledge about physical culture and sports, hygiene, etc.; use of interactive tasks; conducting fitness battles and other activities.
 The advantages of forming digital competences in the process of sports and health activities for older teenagers are: formation of critical and analytical thinking skills; popularization of a healthy lifestyle through the use of digital technologies; development of information literacy and data processing skills; development of search activity; development of cooperation and interaction skills; the opportunity to be involved in sports and health activities (increase one's own physical health indicators) in the online mode.

Leveraging Big Data Analytics for Urban Planning: A Study Using the Big Data Analytics Efficiency Test

Data from a variety of sample cities was evaluated as part of a research looking into the integration of big data analytics into urban planning. The goals were to evaluate the impact of data analytics infrastructure, data volume and processing time, urban development initiatives, and data analytics efficiency. The results showed significant differences in data analytics resources across cities, indicating different levels of investment and preparedness for data-driven decision making. It was clear that cities could handle large amounts of data efficiently thanks to their strong data processing skills. Data analytics have an impact on urban development initiatives, highlighting the revolutionary potential of data-driven urban planning. The outcomes of efficiency tests demonstrated how data analytics procedures are useful for improving urban services and for making well-informed judgments. This study offers important new insights into the mechanics of data-driven urban planning and how it can influence how cities develop in the future.

Big Data in strategic management accounting

The importance of the influence of Industry 4.0 and the digitalization of the economy on the development of the accounting system in modern conditions has been grounded. The role of Big Data in solving key accounting problems and increasing the relevance of accounting information has been grounded. The reasons for the relevance of using Big Data in management accounting have been analyzed. The problems caused by using Big Data in management accounting have been identified and analyzed. The need to develop management accountants’ abilities and skills in data processing and presentation using Big Data tools has been grounded. The role of big data in ensuring the digital transformation of the enterprise’s strategic management system has been analyzed. Directions for using Big Data in strategic management accounting have been identified and described (innovative development of the system; improvement of strategic control; improvement of particular accounting methods; improvement of accounting methodology; change in the role of strategic management accountants).

6 Back-To-Drive Project. Assessment of Fitness-To-Drive in Older and Cognitively Impaired Adult Drivers: Adaptation of the on-Road Driving Observation Schedule to Simulation-Based Environments

Objective:Cognitive, motor and sensory deficits associated with aging, and with some neurological conditions such as acquired brain injury, may lead to severe driving performance impairment. While rehabilitation and driver assistance technologies may improve driving performance, the assessment of the actual fitness-to-drive of these people is challenging. Office-based neuropsychological/physical tests are considered insufficient to understand one’s ability to drive. The gold standard is the on-road assessment with dual control cars, superior in ecological validity, but expensive, stressful, and potentially unsafe. Valid, more cost-effective solutions for a safer, more accurate, standardized assessment of fitness-to-drive are currently needed. Modern and sensorized driving simulators offer key advantages, such as the possibility of exposing drivers to several relevant driving scenarios, including hazard situations, and of assessing their driving performance without being physically at risk. However, the extraction and direct interpretation of existing simulator-produced data may require specialized data processing skills or simulation expertise. To overcome this, we have developed an easy-to-use, pencil-and-paper observational instrument. The Sim-DOS is an adaptation of the widely used instrument to assess “natural driving”, the Driving Observation Schedule (DOS; Vlahodimitrakou et al., 2013).Participants and Methods:Via expert consensus, DOS targeted behaviors were adapted to a simulated-based environment (signaling, observation of environment, speed regulation, slow or unsafe reaction, distance interpretation, vehicle/lane positioning), and the Sim-DOS scores calculation (based on errors while doing such behaviors) was adapted from DOS to include hazard situations (HS, 0-100) and free driving (FD, 0-°°) scores. The instrument was then piloted with a sample of 35 older adults, along with the collection of simulator-produced data on number of harsh events and driving speed. Participants drove two consecutive 20-minutes long scenarios, with low and high traffic density (LTD, HTD). In each scenario, there were periods with and without potentially hazard situations.Results:Assessments were performed by two independent trained observers, producing substantial inter-rater reliability (intra-class correlation coefficients above 0.94). Participants (70.7±4.1 years old, 60% male, 46.1±6.7 years of driving experience) were mostly regular drivers (74%). However, psychomotor skills of the majority were compromised, with only one participant being above the 80th percentile in the reaction times test of the national mandatory driving assessment. When exposed to hazard situations, most of the participants (94.1%) did not perform well, independently of traffic density, with average Sim-DOS-HS scores of 87.1±9.7 (out of 100, t-values>7.3, p-values<.05).Compared to LTD scenarios, in HTD scenarios participants drove less smoothly (HTD:0.97±1.24 vs. LTD:0.33±0.58 of harsh events, Z=3.1, p<.05). However, they also drove slower (HTD:82.41±27.43 vs. LTD:103.55±14.61 km/h, t=5.2, p<.05), improving their ability to manage hazard situations, and therefore producing higher than expected Sim-DOS scores (HTD:87.05±10.28). During free driving, participants performed worse under LTD conditions (Sim-DOS-FD scores: HTD:11.68±6.20 vs. LTD:14.40±9.58, t=2.15, p<.05) as they drove at higher speed (HTD:85.01±24.28 vs. LTD:104.70±11.94 km/h, t=5.8, p<.05), although they did it more smoothly (HTD:1.94±3.74 vs. LTD:0.45±0.74 harsh events, Z=2.65, p<.05).Conclusions:Our study provides a validated driving assessment tool for use in driving simulators that will allow a safer, more ecologic, holistic and informative evaluation of the fitness-to-drive of older adults and neurological patients.

Assessing and validating the specialized competency framework for pharmacists in sales and marketing (SCF-PSM): a cross-sectional analysis in Lebanon

ObjectivesCompetencies refer to the knowledge, skills, attitudes, and behaviors individuals develop through education, training, and experience. These competencies can be formulated into a framework to support practitioner development for effective and sustained performance. In the absence of a national framework for pharmacy education and practice in Lebanon, the Order of Pharmacists of Lebanon (OPL, the official association of pharmacists in Lebanon) pioneered the development of a pharmacy competency framework in 2017. This study aimed to validate and assess the specialized competency framework for pharmacists in sales and marketing (SCF-PSM) after updating the framework previously published by the OPL. The secondary objective was to assess, in a pilot survey, the personal characteristics associated with these competencies.MethodsAfter validating the content of the specialized competency framework, a survey involving Lebanese pharmacists was performed through a 15-min online questionnaire distributed over social media platforms, groups of pharmacists, and individual pharmacists’ contact numbers.Key findingsPharmaceutical knowledge, communication, emergency response, and operation management during emergencies were satisfactory (more than 80/100). Other activities during emergencies, such as patient care and population health interventions and evaluation, research, and dissemination of research outcomes, received a moderate score (75–78/100), similar to legal practice (78/100), teamwork (76/100), and management skills (75/100). The lowest reported confidence was related to professional communication skills (other than communication per se), mainly negotiation, data processing skills, information technology, self-management, and ethical practice (< 75/100). This study reported deficiencies between what is acquired during undergraduate, postgraduate, and continuing education on the one hand and the competency framework suggested by the OPL on the other hand, showing a mismatch between the competencies of working pharmacists acquired during education and the market needs.ConclusionsThis study validated a competency framework for pharmacists in sales and marketing and explored the current gaps in self-reported competencies. It also identified areas necessitating reinforcement to optimize professional practice and underscored the need for improvement in undergraduate, postgraduate, and continuing professional education.

Recent Applications of Smart Technologies for Monitoring the Sustainability of Forest Operations

Precision forestry is a useful technique to help forest stakeholders with proper sustainable forest management. Modern sensors and technologies, with special reference to the sustainability of forest operations, can be applied on a variety of levels, including the monitoring of forest activities regarding the three pillars (economy, environment, and society). In this review, we summarised the current level of knowledge regarding the use of precision forestry techniques for monitoring forest operations. We concentrated on recent data from the last five years (2019–2023). We demonstrated how an Industry 4.0 strategy for remote and proximal monitoring of working performance can be effective when using CAN-bus and StanForD data collected by modern forest machines. The same information can be effectively used to create maps of soil trafficability and to evaluate the patterns of skid tracks or strip roads built as a result of forest intervention. Similar information can be gathered in the case of small-scale forestry by using GNSS-RF (Global Navigation Satellite Systems—Radio Frequency) or even monitoring systems based on smartwatches or smartphones. LiDAR and Structure for Motion (SfM) photogrammetry are both useful tools for tracking soil rutting and disturbances caused by the passage of forest machinery. SfM offers denser point clouds and a more approachable method, whereas laser scanning can be considerably faster but needs a more experienced operator and better data-processing skills. Finally, in terms of the social component of sustainability, the use of location sharing technologies is strongly advised, based for instance on GNSS—RF to monitor the security of forest workers as they operate.

Deep learning-based vehicle detection from orthophoto and spatial accuracy analysis

Deep Learning algorithms are used by many different disciplines for various purposes, thanks to their ever-developing data processing skills. Convolutional neural network (CNN) are generally developed and used for this integration purpose. On the other hand, the widespread usage of Unmanned Aerial Vehicles (UAV) enables the collection of aerial photographs for Photogrammetric studies. In this study, these two fields were brought together and it was aimed to find the equivalents of the objects detected from the UAV images using deep learning in the global coordinate system and to evaluate their accuracy over these values. For these reasons, v3 and v4 versions of the YOLO algorithm, which prioritizes detecting the midpoint of the detected object, were trained in Google Colab’s virtual machine environment using the prepared data set. The coordinate values read from the orthophoto and the coordinate values of the midpoints of the objects, which were derived according to the estimations made by the YOLO-v3 and YOLOV4-CSP models, were compared and their spatial accuracy was calculated. Accuracy of 16.8 cm was obtained with the YOLO-v3 and 15.5 cm with the YOLOv4-CSP. In addition, the mAP value was obtained as 80% for YOLOv3 and 87% for YOLOv4-CSP. F1-score is 80% for YOLOv3 and 85% for YOLOv4-CSP.

Precision comparison for Different GNSS PPP solution using online services and open-source software processing

In order to properly post-process data from the Global Navigation Satellite System, it is crucial to evaluate the dependability by means of online free processing tools because the development of GNSS offers satellite navigation consumers around the globe promising benefits. The purpose of the study is to assess the precision of several online processing tools, including GSIPOST, AUSPOS, IBGE, Magic GNSS, CSRS-PPP, and open-source software. Three stations' worth of RINEX 2 data were subjected to field observations utilising GNSS observations from IGS, with an observation time of 30 seconds. After that, both online and open-source software was used to analyze the collected data, using GPS and GPS and GLONASS. The relative differences and accuracy of the coordinates generated by each software were then assessed by comparison with those acquired from reference stations. coordinates in the ITRF14 standard for the X, Y, and Z directions. Online GNSS processing services don't require GNSS data processing skills and are easy to use, and provide more accurate results for engineering and geodetic uses. RINEX 3 data must be checked for better resolution.

Sleepy Mice Case Study: Implementation and Assessment.

Case studies are a valuable teaching tool to engage students in course content using real-world scenarios. As part of the High-throughput Discovery Science & Inquiry-based Case Studies for Today's Students (HITS) Research Coordination Network (RCN), our team has created the Sleepy Mice Case Study for students to engage with RStudio and the Allen Institute for Brain Science's open access high-throughput sleep dataset on mice. Sleep is important for health, a familiar concern to college students, and was a basis for this case study. In this case, students completed an initial homework assignment, in-class work, and a final take-home application assignment. The case study was implemented in synchronous and asynchronous Introductory Neuroscience courses, a Biopsychology course, and a Human Anatomy and Physiology course, reflecting its versatility. The case can be used to teach course-specific learning objectives such as sleep-related content and/or science data processing skills. The case study was successful as shown by gains in student scores and confidence in achieving learning objectives. Most students reported enjoying learning about sleep deprivation course content using the case study. Best practices based on instructor experiences in implementation are also included to facilitate future use so that the Sleepy Mice Case Study can be used to teach content and/or research-related skills in various courses and modalities.

Pelatihan Aplikasi NVivo untuk Riset Kualitatif Bidang Akuntansi kepada Para Peneliti di Universitas Dhyana Pura

The purpose of implementing this community service program is to improve the ability of researchers to conduct qualitative research using the NVivo 12 plus software. This activity was carried out through three stages, namely the planning stage, the implementation stage and the evaluation stage. The method used in the implementation of the event is the lecture and practice method. The resource persons who became instructors in this training were lecturers at the Universitas 17 Agustus 1945 Jakarta and lecturers at Universitas Esa Unggul, as a form of community service. This training is really needed by researchers at Dhyana Pura University because no one has mastered the use of this software properly. Data processing skills with the NVivo 12 Plus software are needed to help the process of completing qualitative research research more effectively. This training focuses on the use of software for processing data from interviews, processing data from news portals, YouTube social media and literature reviews. Evaluation was carried out at the end of the event by distributing questionnaires in the form of a Google form, to find out the level of mastery of the participants. There was an increase in software mastery for the majority of the training participants. The majority of participants were able to independently process data using the NVivo 12 plus software.

OntoloViz: a GUI for interactive visualization of ranked disease or drug lists using the MeSH and ATC ontologies.

Structured vocabularies for drugs and diseases represent, besides their primary use for annotating scientific literature or scientific information in general, a valuable resource for visualizing aggregated information. The Medical Subject Headings (MeSH) and Anatomical Therapeutic Chemical (ATC) ontologies are widely used structured vocabularies for diseases and drugs, respectively. Their hierarchical tree-like structure can be used as a basis for creating intuitive visual displays for specific diseases and drugs within their higher-order classifications. Such displays are helpful means to contextualize diseases and drugs in various settings such as in drug repositioning. However, there are few tools that can harness the potential of these structured ontologies to create informative visual representations without extensive programming and data processing skills. We have developed OntoloViz, a Graphical User Interface (GUI) for visualizing annotated lists of drugs or diseases in the context of their MeSH or ATC ontologies in an intuitively interpretable sunburst layout. Minimum input is a list of disease or drug names. Users in addition have the option to specify numerical parameters for the input lists to enhance the visualization, e.g. to visualize term frequencies. The GUI allows values to be propagated upwards in the respective ontology tree structure thus facilitating exploration of gene and drug lists. We present two use cases for OntoloViz, namely (i) a graphical representation of clinically tested drugs for coronavirus disease (COVID-19) based on ATC Classification and (ii) a graphical representation of literature annotation of human diseases on the MeSH ontology. The OntoloViz package can be retrieved from PyPi. The source code along with test data, template, and documentations are available at GitHub (https://github.com/Delta4AI/OntoloViz).

ANALYSIS OF INFORMATION PROCESSING CAPABILITIES AND DIGITAL DATA ON DIGITAL LITERACY SKILLS OF ELEMENTARY SCHOOL STUDENTS

Digital technology in the 21st century is growing rapidly, especially at the elementary school level. But in reality some students have not been able to adapt to technology and minimal digital literacy skills. As a result, students have difficulty in choosing valid information on digital media. Therefore, students must have digital information and data processing skills. These skills are needed to filter information and data obtained using digital technology in order to be able to analyze information from digital sources accurately and can be justified. This study aims to determine the level of digital data and information processing ability in digital literacy skills of elementary school students. This study uses a descriptive quantitative approach with a survey method, with data collection through questionnaires and online case study test questions to a sample of 151 high school elementary school students. The data analysis technique used in this research is descriptive statistical technique. The results of this study indicate that the ability to process information and digital data on digital literacy skills of students from 4 elementary schools in East Java is still in the sufficient category, so it still needs to be improved.

Long distance high resolution FMCW laser ranging with phase noise compensation and 2D signal processing.

A long distance high resolution frequency-modulated continuous wave (FMCW) laser rangefinder with phase noise compensation and two-dimensional (2D) data processing skills is developed. Range-finding ladar consists of a continuously chirped laser source, an auxiliary reference interferometer, and a monostatic optical transceiver for target illumination and return photon collection. To extend the range unambiguity and lower the electronic processing bandwidth, a two-step laser frequency chirping scheme is adopted, where a long pulse width, small frequency bandwidth laser chirping signal are used in step 1 for coarse distance estimation, and a short pulse width and large frequency bandwidth laser chirping signal are applied afterwards for step 2 high resolution distance realization. An auxiliary reference interferometer is to record the phase noise originated from the laser source to compensate for phase errors induced in the target return photons. The 2D data processing skill helps to coherently sum up all the phase noise removed echo photons to achieve high resolution range peak extraction with high detection sensitivity. Experimental demonstration shows that the proposed FMCW ladar at 1550 nm wavelength with a laser chirping bandwidth of 10 GHz and electronic processing bandwidth of 200 MHz can measure a corner cube test target in an outdoor atmospheric environment, and the measurement results are 12013.905 m with a 2.4 cm range resolution under strong return photon levels and 12013.920 m with a 2.5 cm range resolution under weak return photon levels.

Encounter with a rectilinear uniformly accelerated motion in a flipped classroom: enhancing students’ data processing skills

Due to the conditions imposed worldwide by the pandemic, students’ access to school laboratories is limited, if not impossible. To provide students with raw experimental data to assess, analyse and reason out, we have filmed experiments that can be used in a flipped classroom. This paper presents an experiment which makes use of an array of six photogate timers track the trajectory of a cart. The students are provided at home with different parts of the video, they process the data and make predictions. During the synchronous distance learning, they present their predictions, compare them with the measured values and discuss their conclusions. In this way, they encounter the predictive power of physics’ laws of motion, address the reliability of their conclusions as a function of the quality and sample size of the experimental data.

Research on the Effect of New Computer Data Processing Technology on the Development of Modern Avionics Equipment

For now, the continuous advancement of computer data processing technology has brought about earth-shaking improvements in the infrastructure and basic technology models of various industries in our society. In the course of the development of modern avionics equipment, the application of data processing skills has firmly grasped the air control authority of aviation electronic equipment. For the future progress of modern avionics, this is an epoch-making breakthrough in computer skills. This article analyzes the development of various aspects of avionics equipment from different perspectives. In addition, this article also conducts a rough analysis and exploration of computer skills related to equipment that can break through modern avionics to help study the practicability of the equipment assisted by it.

Research on Curriculum Reform of Statistics for Accounting Major in the Time of Big Data

Advances in technology have brought enormous challenges to accounting staffs. With the explosive growth of data, the scope of accounting functions is no longer limited to the basic functions such as accounting and checking. Data processing skills are one of the indispensable skills of today's accountants. Changing the traditional statistical teaching framework and increasing the teaching content related to big data helps students to better adapt to new requirements and better integrate to the society.