Rapid Update Research Articles

How to Improve Higher Vocational English Teaching Mode under the Condition of Rapid Update of Modern Technology

Nowadays, in the higher vocational English courses promulgated by the Ministry of Education, students are required to master certain basic knowledge and skills of English. This article begins with an in-depth analysis of English teaching in higher vocational colleges and finds that higher vocational English teaching has made a lot of achievements in recent years. This is reflected in the development of clear teaching guidance documents and the publication of series suitable for higher vocational education. English textbooks, and some new teaching models are being explored. However, in general, there are still many unresolved problems and challenges in higher vocational English teaching, which are mainly manifested in: the implementation of the guiding documents is not in place, the transformation of teachers’ educational and teaching concepts is relatively lagging, and the modernization of teaching models is not high. These problems have affected the effect of higher vocational English teaching, and seriously affected the English training of higher vocational students.

Applying a new integrated mass-flux adjustment filter in rapid update cycling of convective-scale data assimilation for the COSMO model (v5.07)

Abstract. A new integrated mass-flux adjustment filter is introduced, which uses the analyzed integrated mass-flux divergence field to correct the analyzed wind field. The filter has been examined in twin experiments with rapid update cycling using an idealized setup for convective-scale radar data assimilation. It is found that the new filter slightly reduces the accuracy of background and analysis states; however, it preserves the main structure of cold pools and primary mesocyclone properties of supercells. More importantly, it considerably diminishes spurious mass-flux divergence and the high surface pressure tendency, and it thus results in more dynamically balanced analysis states. For the ensuing 3 h forecasts, the experiment that employs the filter becomes more skillful after 1 h. These preliminary results show that the filter is a promising tool to alleviate the imbalance problem caused by data assimilation, especially for convective-scale applications.

Multi-scale deep learning and optimal combination ensemble approach for AQI forecasting using big data with meteorological conditions

Faced with the rapid update of nonlinear and irregular big data from the environmental monitoring system, both the public and managers urgently need reliable methods to predict possible air pollutions in the future. Therefore, a multi-scale deep learning (MDL) and optimal combination ensemble (OCE) approach for hourly air quality index (AQI) forecasting is proposed in this paper, named MDL-OCE model. Before normal modeling, all original data are preprocessed through missing data filling and outlier testing to ensure smooth computation. Due to the complexity of such big data, slope-based ensemble empirical mode decomposition (EEMD) is adopted to decompose the time series of AQI and meteorological conditions into a finite number of simple intrinsic mode function (IMF) components and one residue component. Then, to unify the number of components of different variables, the fine-to-coarse (FC) technique is used to reconstruct all components into high frequency component (HF), low frequency component (LF), and trend component (TC). For purpose of extracting the underlying relationship between AQI and meteorological conditions, the three components are respectively trained and predicted by different deep learning architectures (stacked sparse autoencoder (SSAE)) with a multilayer perceptron (MLP). The corresponding forecasting results of three components are merged by OCE method to better achieve the ultimate AQI forecasting outputs. The empirical results clearly demonstrate that our proposed MDL-OCE model outperforms other advanced benchmark models in terms of forecasting performances in all cases.

Impact of Lightning Data Assimilation on the Short-Term Precipitation Forecast over the Central Mediterranean Sea

Lightning data assimilation (LDA) is a powerful tool to improve the weather forecast of convective events and has been widely applied with this purpose in the past two decades. Most of these applications refer to events hitting coastal and land areas, where people live. However, a weather forecast over the sea has many important practical applications, and this paper focuses on the impact of LDA on the precipitation forecast over the central Mediterranean Sea around Italy. The 3 h rapid update cycle (RUC) configuration of the weather research and forecasting (WRF) model) has been used to simulate the whole month of November 2019. Two sets of forecasts have been considered: CTRL, without lightning data assimilation, and LIGHT, which assimilates data from the LIghtning detection NETwork (LINET). The 3 h precipitation forecast has been compared with observations of the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) (IMERG) dataset and with rain gauge observations recorded in six small Italian islands. The comparison of CTRL and LIGHT precipitation forecasts with the IMERG dataset shows a positive impact of LDA. The correlation between predicted and observed precipitation improves over wide areas of the Ionian and Adriatic Seas when LDA is applied. Specifically, the correlation coefficient for the whole domain increases from 0.59 to 0.67, and the anomaly correlation (AC) improves by 5% over land and by 8% over the sea when lightning is assimilated. The impact of LDA on the 3 h precipitation forecast over six small islands is also positive. LDA improves the forecast by both decreasing the false alarms and increasing the hits of the precipitation forecast, although with variability among the islands. The case study of 12 November 2019 (time interval 00–03 UTC) has been used to show how important the impact of LDA can be in practice. In particular, the shifting of the main precipitation pattern from land to the sea caused by LDA gives a much better representation of the precipitation field observed by the IMERG precipitation product.

Research on the Practical Application of Server Virtualization Technology in Computer Laboratory

At present, most of the university’s conditions and teaching equipment have greater room for improvement, especially the management and teaching mechanism of computer laboratory need further improvement and breakthrough. Due to the rapid update speed of computer information technology related equipment, the update speed of university related equipment can not meet the needs of experimental education. Based on this, this paper first analyzes the application and development status of server virtualization technology, then studies the specific application of server virtualization technology in computer laboratory, and finally gives the application design strategy of server virtualization technology in computer laboratory.

Distributed Beams: Concept of Operations for Polarimetric Rotating Phased Array Radar

Important requirements for a future generation of weather surveillance radars include improvements in data quality and more rapid update of volumetric data. Phased array radar (PAR) is a candidate technology capable of providing the required functionality. The rotating PAR (RPAR) is a potential architecture that could improve the capabilities of the current parabolic-reflector-based US Weather Surveillance Radar—1988 Doppler (WSR-88D) operational network and is more affordable than other candidate PAR architectures. However, RPAR concept of operations that support observational needs has to be developed. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Distributed Beams</i> (DB) technique introduced in this article provides a way to either reduce the scan times or to reduce the variance of radar-variable estimates by azimuthally spoiling the transmit beam while receiving multiple digital beams as the radar rotates in azimuth. Specifically, the rotation speed of the pedestal is derived from the duration of the coherent processing interval (CPI) to produce the desired spatial sampling. This results in beams from subsequent CPIs in approximately the same directions, which increases the number of available data samples for processing. The increased number of available samples can be coherently processed to reduce the variance of estimates. Alternatively, by reducing the number of samples per CPI and increasing the RPAR’s rotation rate, the scan time can be reduced without increasing the variance of estimates. Results presented demonstrate both applications of the DB technique for dual-polarization observations. Given that this technique makes use of spoiled transmit beams, its benefits come at the expense of degraded angular resolution (beamwidth and sidelobe levels), and reduced sensitivity compared with the use of pencil beams. The technique could be implemented as part of an RPAR concept of operations to meet requirements for the future weather surveillance network if certain tradeoffs are accounted for in the radar design process.

Service Innovation of Insurance Data Based on Cloud Computing in the Era of Big Data

With the advent of the era of big data, great changes have taken place in the insurance industry, gradually entering the field of Internet insurance, and a large amount of insurance data has been accumulated. How to realize the innovation of insurance services through insurance data is crucial to the development of the insurance industry. Therefore, this paper proposes a ciphertext retrieval technology based on attribute encryption (HP‐CPABKS) to realize the rapid retrieval and update of insurance data on the premise of ensuring the privacy of insurance information and puts forward an innovative insurance service based on cloud computing. The results show that 97.35% of users are successfully identified in test set A and 98.77% of users are successfully identified in test set B, and the recognition success rate of the four test sets is higher than 97.00%; when the number of challenges is 720, the modified data block is less than 9%; the total number of complaints is reduced from 1300 to 249; 99.19% of users are satisfied with the innovative insurance service; the number of the insured is increased significantly. To sum up, the insurance innovation service based on cloud computing insurance data can improve customer satisfaction, increase the number of policyholders, reduce the number of complaints, and achieve a more successful insurance service innovation. This study provides a reference for the precision marketing of insurance services.

WRF Prediction of Operational Wind Farm Data and Influence of Upwind Farms on Power Production

Accuracy of the Weather Research and Forecasting (WRF) model is determined for prediction of wind resource and power supply of an operational wind farm located in complex terrain in Baja California and the influence of upwind-farm wakes on energy production is assessed. The WRF model initialized with the North American Regional Reanalysis (NARR) provides time-variation and annual occurrence of wind speed within 12% and 1.4%, using either the Rapid Update Cycle (RUC) or Pleim-Xu (PX) land-surface models. Energy supply is within 5.25% (RUC) and 2% (PX) of SCADA data from a 10 MW wind farm. The wind resource at several sites in the region offers the potential for wind farms with capacity factors in the range 32-34%. This reduces to 29% when wind turbine momentum extraction is modelled and the wakes of these farms extend over 12 km downwind, with greater extent for larger-diameter 5 MW turbines than 2 MW turbines. The farm wakes reduce power output from an operational farm by over 20% for wind speeds below rated speed resulting in 1.3-1.7% reduction in annual energy. This analysis establishes confidence in use of numerical weather prediction tools for wind farm operational data and informs use for wind farm planning.

The role of real humanism in the digitalization of schools: methodological aspects

The introduction of digitalization in the educational space causes anambiguous reaction. Some consider it a tool for solving the problems accumulated in school, while others openly define it as a factor in the school dehumanization. In order to understand what digitalization brings to the schools, you need to know the goal of implementing it. The author believes that in the broadest sense school and society form a single whole. In connection with the rapid update of technologies in the production and non-production spheres, there is a need to add managerial relations in the labor sphere with pedagogical ones. Complicated out-of-school conditions for younger generationeducation force the school itself to work more closely in synergy with society, and for this a democratic public schoolmust exist. A school that uses the “digitalization of the educational space” tool will have to implement the function of connecting with the past, determining its place in the present and future. Consequently, the school also performs the function of spiritual confrontation in hybrid wars. Skilful use of the “digitalization of the educational space” will create conditions in which the school can be useful for production and vice versa. The role of school in interaction with society is increasing due to the transformation of the cognition process into the main type of human activity. The tool of digitalization should be used in the environmental education and the entire society, since ecology currently includes all the pressing problems of humanity.

Estimating firm digitalization: A method for disaggregating sector-level digital intensity to firm-level

The digital transformation of firms plays an increasingly important role in the economy and society. However, limited access to data on firm-level digital intensity is an impediment to advancement of multiple research projects concerned with firm digitalization. To alleviate this challenge, this paper proposes a method for estimating firm-level digital intensity based on other more readily available firm-level data and reference data on digitalization, which is available on sector-level. The proposed method utilizes firm-level revenue breakdown by sector to estimate sector revenue-weighted digital intensity scores, which lead to classification of firms into low, medium and high digital intensity groups. The output from the proposed method can be directly used in research concerned with firm digitalization and investigating this multifaceted phenomenon. Results from the application of the proposed method to an illustrative sample of large US and non-US firms (2000 observations in total) indicate that firm-level digital intensity can be efficiently estimated for large samples using data commonly available to researchers.The key differences between the proposed method and alternative methods are:•Recognition of the fact that firms might participate in more than one sector or industry, which partially explains within-sector heterogeneity in firm-level digital intensity. We found that 67.8% of large US firms and 78.6% of large non-US firms were engaged in more than one industry.•Use of reference sector-level digital intensity scores, which allows for rapid update, application across geographies and time, as well as parallel calculation of multiple digital intensity scores for each reference data. Furthermore, use of reference data enables supplementation of firm-level data on digitalization.•Replicability of the method and reproducibility of the results through inclusion of the source code and availability of data through research and commercial databases.

Machine Learning with Feature Importance Analysis for Tornado Prediction from Environmental Sounding Data

AbstractTornadoes pose a forecast challenge to National Weather Service forecasters because of their quick development and potential for life‐threatening damage. The use of machine learning in severe weather forecasting has recently garnered interest, with current efforts mainly utilizing ground weather radar observations. In this study, we investigate machine learning techniques to discriminate between nontornadic and tornadic storms solely relying on the Rapid Update Cycle (RUC) sounding data that represent the pre‐storm atmospheric conditions. This approach aims to provide for early warnings of tornadic storms, before they form and are detectable by weather radar observations. Feature analysis of a Random Forest machine learning model uncovers that the pressure variable has little impact on the classification process, which is consistent with known key physical attributes of tornado formation, demonstrating the ability of machine learning techniques to provide insight solely based on the data.

Recent developments in the data assimilation of AROME/HU numerical weather prediction model

A local three-dimensional variational data assimilation (DA) system was implemented operationally in AROME/HU (Application of Research to Operations at Mesoscale) non-hydrostatic mesoscale model at the Hungarian Meteorological Service (OMSZ) in 2013. In the first version, rapid update cycling (RUC) approach was employed with 3-hour frequency in local upper-air DA using conventional observations only. Optimal interpolation method was adopted for the surface data assimilation later in 2016. This paper describes the current developments showing the impact of more conventional and remote-sensing observations assimilated in this system, which reveals the benefit of additional local high-resolution observations. Furthermore, it is shown that an hourly assimilation-forecast cycle outperforms the 3-hourly updated system in our DA. Besides the upper-air assimilation developments, a simplified extended Kalman filter (SEKF) was also tested for surface data assimilation, showing promising performance on both the analyses and the forecasts of AROME/HU system.

Challenges and considerations on quality control and evaluation of pathogen metagenomic next-generation sequencing

Metagenomic next-generation sequencing (mNGS) could be used for pathogen detection from nearly all types of clinical samples. Especially, the unique diagnostic capability of pathogen mNGS detecting unknown causative agent of infectious diseases makes this method become an importation complement and irreplaceable component for conventional routine laboratory test. However, the complexity of the testing process, the rapid product update, and the insufficiency in quality control and evaluation methods that all make clinical transformation, industry development, and regulation of this technology full of challenge and uncertainty. This review briefly introduces the technical advantages and challenges, and describes the general workflow and quality control steps in details. Finally, it focuses on current considerations regarding quality evaluation methods and standards for pathogen mNGS.

Can Pre-Storm Errors in the Low-Level Inflow Help Predict Spatial Displacement Errors in MCS Initiation?

The Great Plains low-level jet (LLJ) is a contributing factor to the initiation and evolution of nocturnal Mesoscale Convective Systems (MCSs) in the central United States by supplying moisture, warm air advection, and a source of convergence. Thus, the ability of models to correctly depict thermodynamics in the LLJ likely influences how accurately they forecast MCSs. In this study, the Weather Research and Forecasting (WRF) model was used to examine the relationship between spatial displacement errors for initiating simulated MCSs, and errors in forecast thermodynamic variables up to three hours before downstream MCS initiation in 18 cases. Rapid Update Cycle (RUC) analyses in 3 layers below 1500 m above ground level were used to represent observations. Correlations between simulated MCS initiation spatial displacements and errors in the magnitude of forecast thermodynamic variables were examined in regions near and upstream of both observed and simulated MCSs, and were found to vary depending on the synoptic environment. In strongly-forced cases, large negative moisture errors resulted in simulated MCSs initiating further downstream with respect to the low-level flow from those observed. For weakly-forced cases, correlations were weaker, with a tendency for smaller negative moisture errors to be associated with larger displacement errors to the right of the inflow direction for initiating MCSs.

What Works in Implementing Patient Decision Aids in Routine Clinical Settings? A Rapid Realist Review and Update from the International Patient Decision Aid Standards Collaboration.

BackgroundDecades of effectiveness research has established the benefits of using patient decision aids (PtDAs), yet broad clinical implementation has not yet occurred. Evidence to date is mainly derived from highly controlled settings; if clinicians and health care organizations are expected to embed PtDAs as a means to support person-centered care, we need to better understand what this might look like outside of a research setting.AimThis review was conducted in response to the IPDAS Collaboration’s evidence update process, which informs their published standards for PtDA quality and effectiveness. The aim was to develop context-specific program theories that explain why and how PtDAs are successfully implemented in routine healthcare settings.MethodsRapid realist review methodology was used to identify articles that could contribute to theory development. We engaged key experts and stakeholders to identify key sources; this was supplemented by electronic database (Medline and CINAHL), gray literature, and forward/backward search strategies. Initial theories were refined to develop realist context-mechanism-outcome configurations, and these were mapped to the Consolidated Framework for Implementation Research.ResultsWe developed 8 refined theories, using data from 23 implementation studies (29 articles), to describe the mechanisms by which PtDAs become successfully implemented into routine clinical settings. Recommended implementation strategies derived from the program theory include 1) co-production of PtDA content and processes (or local adaptation), 2) training the entire team, 3) preparing and prompting patients to engage, 4) senior-level buy-in, and 5) measuring to improve.ConclusionsWe recommend key strategies that organizations and individuals intending to embed PtDAs routinely can use as a practical guide. Further work is needed to understand the importance of context in the success of different implementation studies.

Improving Afternoon Thunderstorm Prediction over Taiwan through 3DVar-Based Radar and Surface Data Assimilation

AbstractRecently, the Central Weather Bureau of Taiwan developed a WRF- and WRF data assimilation (WRFDA)-based convective-scale data assimilation system to increase model predictability toward high-impact weather. In this study, we focus on afternoon thunderstorm (AT) prediction and investigate the following questions: 1) Is the designation of a rapid update cycle strategy with a blending scheme effective? 2) Can surface data assimilation contribute positively to AT prediction under the complex geography of Taiwan island? 3) What is the relative importance between radar and surface observation to AT prediction? 4) Can we increase the AT forecast lead time in the morning through data assimilation? Consecutive ATs from 30 June to 8 July 2017 are investigated. Five experiments, each having 240 continuous cycles, are designed. Results show that employing continuous cycles with a blending scheme mitigates model spinup compared with downscaled forecasts. Although there are few radar echoes before AT initiation, assimilating radar observations is still crucial since it largely corrects model errors in cycles. However, assimilating surface observations is more important compared with radar in terms of extending forecast lead time in the morning. Either radar or surface observations contribute positively, and assimilating both has the highest QPF score. Assimilating surface observations systematically improves surface wind and temperature predictions based on 240 cases. A case study demonstrates that the model can capture the AT initiation and development by assimilating surface and radar observations. Its cold pool and outflow boundary prediction are also improved. In this case, the assimilation of surface wind and water vapor in the morning contributes more compared with temperature and pressure.

UAS Imagery-Based Mapping of Coarse Wood Debris in a Natural Deciduous Forest in Central Germany (Hainich National Park)

Dead wood such as coarse dead wood debris (CWD) is an important component in natural forests since it increases the diversity of plants, fungi, and animals. It serves as habitat, provides nutrients and is conducive to forest regeneration, ecosystem stabilization and soil protection. In commercially operated forests, dead wood is often unwanted as it can act as an originator of calamities. Accordingly, efficient CWD monitoring approaches are needed. However, due to the small size of CWD objects satellite data-based approaches cannot be used to gather the needed information and conventional ground-based methods are expensive. Unmanned aerial systems (UAS) are becoming increasingly important in the forestry sector since structural and spectral features of forest stands can be extracted from the high geometric resolution data they produce. As such, they have great potential in supporting regular forest monitoring and inventory. Consequently, the potential of UAS imagery to map CWD is investigated in this study. The study area is located in the center of the Hainich National Park (HNP) in the federal state of Thuringia, Germany. The HNP features natural and unmanaged forest comprising deciduous tree species such as Fagus sylvatica (beech), Fraxinus excelsior (ash), Acer pseudoplatanus (sycamore maple), and Carpinus betulus (hornbeam). The flight campaign was controlled from the Hainich eddy covariance flux tower located at the Eastern edge of the test site. Red-green-blue (RGB) image data were captured in March 2019 during leaf-off conditions using off-the-shelf hardware. Agisoft Metashape Pro was used for the delineation of a three-dimensional (3D) point cloud, which formed the basis for creating a canopy-free RGB orthomosaic and mapping CWD. As heavily decomposed CWD hardly stands out from the ground due to its low height, it might not be detectable by means of 3D geometric information. For this reason, solely RGB data were used for the classification of CWD. The mapping task was accomplished using a line extraction approach developed within the object-based image analysis (OBIA) software eCognition. The achieved CWD detection accuracy can compete with results of studies utilizing high-density airborne light detection and ranging (LiDAR)-based point clouds. Out of 180 CWD objects, 135 objects were successfully delineated while 76 false alarms occurred. Although the developed OBIA approach only utilizes spectral information, it is important to understand that the 3D information extracted from our UAS data is a key requirement for successful CWD mapping as it provides the foundation for the canopy-free orthomosaic created in an earlier step. We conclude that UAS imagery is an alternative to laser data in particular if rapid update and quick response is required. We conclude that UAS imagery is an alternative to laser data for CWD mapping, especially when a rapid response and quick reaction, e.g., after a storm event, is required.

Development of Land Cover Classification Model Using AI Based FusionNet Network

Prompt updates of land cover maps are important, as spatial information of land cover is widely used in many areas. However, current manual digitizing methods are time consuming and labor intensive, hindering rapid updates of land cover maps. The objective of this study was to develop an artificial intelligence (AI) based land cover classification model that allows for rapid land cover classification from high-resolution remote sensing (HRRS) images. The model comprises of three modules: pre-processing, land cover classification, and post-processing modules. The pre-processing module separates the HRRS image into multiple aspects by overlapping 75% using the sliding window algorithm. The land cover classification module was developed using the convolutional neural network (CNN) concept, based the FusionNet network and used to assign a land cover type to the separated HRRS images. Post-processing module determines ultimate land cover types by summing up the separated land cover result from the land cover classification module. Model training and validation were conducted to evaluate the performance of the developed model. The land cover maps and orthographic images of 547.29 km2 in area from the Jeonnam province in Korea were used to train the model. For model validation, two spatial and temporal different sites, one from Subuk-myeon of Jeonnam province in 2018 and the other from Daseo-myeon of Chungbuk province in 2016, were randomly chosen. The model performed reasonably well, demonstrating overall accuracies of 0.81 and 0.71, and kappa coefficients of 0.75 and 0.64, for the respective validation sites. The model performance was better when only considering the agricultural area by showing overall accuracy of 0.83 and kappa coefficients of 0.73. It was concluded that the developed model may assist rapid land cover update especially for agricultural areas and incorporation field boundary lineation is suggested as future study to further improve the model accuracy.

Rapid scientific update response to public health professions during Phase I of COVID-19 pandemic

IssuePreparedness can be considered the Achilles heel in Western Coutries to adequately fight Sars-CoV-2 pandemy. The Istituto Superiore di Sanità (ISS) has been challenged to give rapid technical and scientific advices to the State and Regions on a huge variety of Sars-CoV-2 aspects related to public health.Description of the problemRapid and timely accumulation of knowledge and empirical evidence is critical for policymakers and emergency managers to maximally inform their decisions through scientific evidence and to better guide and mobilise the expertise of academics and general practitioners towards effective solutions.ResultsAn ad hoc “Scientific Literature working group” at ISS in the period 23/03 - 31/05 screened a total of 4,568 pre-prints and 15,590 peer reviewed papers extracted from PubMed, arXiv.org, medRxiv and bioRxiv, which gave rise to the following deliverables: a) a daily pre-prints alert selection delivered to ISS President for the routine national Scientific Technical Committee meetings on Sars-CoV-2 chaired by Italian Civil Protection (still ongoing), and, b) a weekly open access issue of Covid Contents publications (8 volumes, https://www.iss.it/en/covid-contents), as summary of most interesting peer-reviewed papers for public health professions.LessonsThe needed cross-cutting approach is leading to: interdisciplinarity enhancement within the ISS; coverage of broad expertise areas and interest of health care providers; cross-linkage between the different aspects/disciplines involved in this pandemic and share of experiences.Key messages Committed Institutions have the responsibility to support practitioners and decision makers to understand relevant aspects of medical, physical, occupational and public safety, in case of emergency.The Silos culture has to be overcome. There is nothing more powerful in any Institution than having all researchers rowing fiercely in the same direction.

Telehealth mental health services during COVID-19: summary of evidence and clinical practice.

Objective:To provide a rapid clinical update on the evidence for telehealth in mental healthcare in the context of the COVID-19 pandemic public health measures.Conclusions:Telehealth has been rapidly implemented in metropolitan and rural settings and the existing evidence base demonstrates that it represents an effective mode of service delivery.