Long-period Data Research Articles

The impact of monetary policy on income inequality: Does inflation targeting matter?

Does inflation targeting affect the nexus between monetary policy and income inequality? We address this question by estimating country-specific structural vector autoregression models across the G7 economies. We find that contractionary monetary policy shocks increase income inequality when using a long period of data from 1974–2019. However, controlling for the adoption of inflation targeting reduces the size of this effect in Japan and the UK, and it disappears altogether in Canada and the US. This suggests that country-specific inflation targeting may mitigate the inequality-inducing effects of unanticipated monetary contractions. In contrast, mixed evidence is found for Eurozone countries: France, Germany, and Italy.

Earth Observation Capacity of the National Geoinformation Center of Bulgaria as Part of the Tools for Geo- and Anthropogenic-Hazard Management in EPOS

Exploring the nature of the Earth system and its multi-scale architecture requires an integrated symbiotic approach not only to understand our planet but also to reveal the hazards and footprints of their events on human life. Following the conceptual model of the European Plate Observing System (EPOS) we integrate a variety of research to obtain a multilateral use of geoinformation for scientific and practical purposes. In 2018, we started building a Bulgarian distributed scientific infrastructure called the National Geoinformation Center, aiming to provide data, products, and expertise to predict and prevent natural and anthropogenic risks and disasters, as well as environmental changes. The present paper demonstrates the capabilities of seismological, Global Navigation Satellite System (GNSS), geomagnetic and multi-scale laboratories (MSL) in Bulgaria that participate to the respective thematic core services of EPOS. More than 40 seismological stations register and transmit data in real time through the operational center of the Bulgarian Seismological Network. Data from 18 GNSS stations, part of the National GNSS network, are officially registered in the EPOS GNSS Data Gateway. Data and products are available via the EPOS GNSS Product Portal for interested users. Panagjurishte geomagnetic observatory (PAG) provides real time variations and long-period data series for the Earth’s magnetic field. The Palaeomagnetic Laboratory at the National Institute of Geophysics, Geodesy and Geography (NIGGG) participates in MSL with its unique facilities for paleo-, archeo- and environmental magnetism studies. Four case studies are presented to illustrate best practices for utilizing the data collected. The analysis of seismicity in a region of a long-time exploited salt deposit is upgraded with GNSS data to reveal the reason for crustal movements in the region. The results show that the majority of the seismic events took place in the upper part of the Earth’s crust at a depth of up to 5 km, where the salt body is buried. The observed significant local deformations of the Earth’s surface are very likely related to the technological process of deposit exploitation. A recent model of the geomagnetic declination over the Bulgarian territory is also presented. It tracks the variation of the regional magnetic field from 2015 to 2020 and adds short-wavelength signals from the secular network observations. Last but not least, the application of a mineral magnetic laboratory analysis along the depth of a Holocene soil profile is demonstrated. Results reveal the soil evolution in response to environmental changes adding a new magnetic proxy parameter for more precise identification and characterization of the soil formation processes.

Executive compensation: A justified reward or a mis-fortune, an empirical analysis of banks in Pakistan

The relationship between compensation, performance, and risk in a sample of Pakistani banks, using panel data for 20 banks from 2011 to 2021 has been examined in this research. To the best of researcher’s knowledge, this is one of the first study examining the relationship between performance, risk and employee compensation using panel data approach in the context of Pakistan and using data for longer period. To examine the influence of employee compensation on the performance and risk of banks, we employed three performance measures: return on assets, return on equity, and operating profits. Risk assessment was conducted using non-performing loans and risk-weighted assets. Findings of study suggest that employee compensation has no significant impact on performance and risk of banks in Pakistan; this means in Pakistan higher employee’s compensation does not enable banks to improve their performance or enable banks to take more risk.

Convolutional Autoencoder-Based Anomaly Detection for Photovoltaic Power Forecasting of Virtual Power Plants

Machine learning-based time-series forecasting has recently been intensively studied. Deep learning (DL), specifically deep neural networks (DNN) and long short-term memory (LSTM), are the popular approaches for this purpose. However, these methods have several problems. First, DNN needs a lot of data to avoid over-fitting. Without sufficient data, the model cannot be generalized so it may not be good for unseen data. Second, impaired data affect forecasting accuracy. In general, one trains a model assuming that normal data enters the input. However, when anomalous data enters the input, the forecasting accuracy of the model may decrease substantially, which emphasizes the importance of data integrity. This paper focuses on these two problems. In time-series forecasting, especially for photovoltaic (PV) forecasting, data from solar power plants are not sufficient. As solar panels are newly installed, a sufficiently long period of data cannot be obtained. We also find that many solar power plants may contain a substantial amount of anomalous data, e.g., 30%. In this regard, we propose a data preprocessing technique leveraging convolutional autoencoder and principal component analysis (PCA) to use insufficient data with a high rate of anomaly. We compare the performance of the PV forecasting model after applying the proposed anomaly detection in constructing a virtual power plant (VPP). Extensive experiments with 2517 PV sites in the Republic of Korea, which are used for VPP construction, confirm that the proposed technique can filter out anomaly PV sites with very high accuracy, e.g., 99%, which in turn contributes to reducing the forecasting error by 23%.

Acoustic monitoring to evaluate the effect of anthropogenic noise within a park

The aim of this paper is to propose the use of passive acoustic monitoring (PAM) as a non-invasive method to investigate the state of communities and ecosystems. PAM operates through the study and characterization of the soundscape of an area. One of the three components of the soundscape (beside geophony and biophony) is anthrophony, which is the collection of sounds produced by human activities. This kind of sounds can have effects on natural envi-ronments and natural population. In this study, recording instruments and sampling tech-niques have been used to acquire and collect sound data for long periods (two weeks) in a nat-ural terrestrial ecosystem (Ticino Park) which is affected by road and rail traffic noise. The analysis conducted studied the trends of the eco-acoustic indices belonging to three measure-ment sites to detect the presence of characteristic trends and to evaluate the influence of the two anthropogenic noise sources at different distances.

Capital Structure and Firm Profitability in Developing Countries

This research intended to investigate the influence of capital structure on private hospitals’ performance using a sample of 162 patients. The researchers used a quantitative approach to evaluate the impact of capital structure (Corporate governance, Ownership structure, and Constructive thought patterns) to measure the influence on sustained competitive advantage in private hospitals in North America. The research sample was chosen using a random sampling method and carried out in various locations in North America. A total of 175 questionnaires were issued, but only 162 participants completed them correctly. The results showed that the most effective relationship was between corporate governance as element of capital structure with firm performance, on the other hand the less effective relationship was between ownership as element of capital structure with firm performance. In addition, our results also suggest that the finance managers should use debts as last alternative in their capital structure. To the end, our study can be extended by incorporating more controlled variables, larger sample and longer period data in the regression models to get better results. Other measures and methodology can also be employed. 

Synthetic magnetotelluric modelling of a regional fault network – implications for survey design and interpretation

The magnetotelluric (MT) method is increasingly being applied to mineral exploration under cover with several case studies showing that mineral systems can be imaged from the lower crust to the near surface. Driven by this success, the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP) is delivering long-period data on a 0.5° grid across Australia, and derived continental scale resistivity models that are helping to drive investment in mineral exploration in frontier areas. Part of this investment includes higher-resolution broadband MT surveys to enhance the resolution of features of interest and improve targeting. To help gain best value for this investment it is important to understand the ability and limitations of MT to resolve features on different scales. Here we present synthetic modelling of continuous, narrow, near-vertical faults 500 m to 1500 m wide with a resistivity 100–200 times less than the background rock resistivity using the ModEM software, and show that for such a situation a station spacing of around 14 km across strike is sufficient to resolve these into the upper crust. However, the vertical extent of these features is not well constrained, with near-vertical planar features commonly resolved as two separate features. This highlights the need for careful interpretation of anomalies in MT inversion. In particular, in an exploration scenario, it is important to consider that a lack of interconnectivity between a lower crustal/upper mantle conductor and conductors higher up in the crust and the surface might be apparent only, and may not necessarily reflect reduced mineral prospectivity.

Use of the Magnetotelluric Method in Brazil and Future Challenges

The magnetotelluric (MT) method is based on measurements of natural electric and magnetic fields at the Earth’s surface to probe the subsurface electrical resistivity structure. A summary of the use of the method in Brazil is presented, with emphasis on the initial theoretical and experimental developments for its implementation in the country. The expansion of the method in recent years is related both to academic studies on the tectonic evolution of geological provinces and to contracted surveys mainly restricted to support oil exploration. Two major challenges are proposed to increase the contribution of the method in the next years. One is the planning and installation of a large experimental program to obtain long-period data from stations installed in a 3D grid. It can contribute to improve the knowledge of the tectonic evolution of the Brazilian shield and also to locate new areas with potential for exploration of mineral resources. Coupled with this expansion of field measurements, another challenge is the need to formulate new data processing techniques. The main objective will be to minimize the effects of coherent and intermittent EM noise related to the installation in the country of electric power transmission networks using high voltage direct currents (HVDC).

Extreme weather events and their impacts on malaria: historical records from the Nordic countries

BACKGROUND AND AIM Warm summer temperature has been associated with historical malaria incidence in the Nordic countries with cool temperate climate, e.g., Finland, Sweden and Denmark. However, other weather factors, especially climatic extreme events and their impacts on malaria outbreak have rarely been rigorously studied. For example, coastal flooding caused by extreme sea level rise, could rapidly alter the larval habitat of brackish water malaria vectors. Other hydrological events like extreme rainfall and severe drought are also likely to impose nonlinear exposure responses and delayed effects of the climate–malaria relationship. This study aims to evaluate the historical malaria occurrence related to extreme precipitation events, and to distinguish the impact of different climate forces on malaria outbreaks by investigation whether there is statistically significant attribution of individual climate extremes to malaria epidemics. METHODS Historical symptom-based malaria cases during the 17-18th centuries were obtained from the Nordic countries, to compare with local long-term meteorological observations of temperature, precipitation and sea level. The non-linear relationship and delayed effect of precipitation events and malaria occurrence were further explored using the distributed lag nonlinear model (DLNM). RESULTS From long-period data of the Nordic countries, this study intends to investigate whether the magnitude of historical malaria epidemics was affected by local coastal flooding which mostly occurred during storm surges. CONCLUSIONS This study highlights the critical importance to distinguish the impacts induced by climate change perturbations, such as extreme rainfall and drought, and in particular coastal flooding, in order to provide a better understanding of diverse effects of climate change on the burden of malaria. KEYWORDS Malaria, Nordic, sea level rise, extreme rainfall, drought, DLNM

Modeling and Forecasting of Rainfall Trends based on Historical Data in Bungoma County, Western Kenya using Holt Winters Method

Rainfall is termed as a meteorological phenomenon which is very useful for daily human activities. Most of the population depend on it for domestic purpose and agriculture, hence it is vital for farmers to know rainfall trends and patterns prevailing in their locality. In Kenya, rainfall variability has caused hunger to many people. In agriculture section, water problem is the most critical constraint in food productions. Extreme weather conditions which is occasioned by climatic change and weather variability makes a small scale farmer in Bungoma to face very high risks of reduced productivity. Scarcity of water is a severe environmental constraint to plant productivity hence drought causes loss in crop yield. Majority of the population in Bungoma do depend on agriculture either directly or indirectly therefore analysis of rainfall data for long periods will provide a lot of information about rainfall variability and this will help to better the agricultural activities of Bungoma farmers. The main aim of the study was to model rainfall patterns and hence predict the future rainfall trends in Bungoma region using Holt winters method in the context of the time series. This is because time series analysis plays an important role in modelling, predicting and forecasting meteorological data such as humidity, temperature, rainfall and other environmental variable. Therefore, data for Bungoma monthly and yearly rainfall patterns for the period 1988-2021 was obtained from the Kenya meteorological department. Collected data was analyzed using Holt winters method by R software. Rainfall data was found to be seasonal implying that most of the rainfall occurred in a specific period each year. Forecasted rainfall had increasing and decreasing prediction intervals and this implied that rainfall could either start decreasing or increasing. The data was found to be non-stationary due to presence of seasonality and rainfall trends. Findings of the study will make it possible to facilitate planning and management of water for both domestic and agricultural use in Bungoma region.

Attendance Management System Using Face Recognition

Attendance Monitoring is a common daily activity in Schools, Colleges, Workplaces and Managed Workplaces. It involves an Attendance gathering entity, a physical storage and the attendees. This task of manual Acquisition of attendance data is often tedious and hectic as well as time consuming assuming the limited Workplace time frame. Living in an age of automated machines, it is only feasible that this task be automated as well. This Project deals with using facial data of students for attendance detection systems and using a digital database to store the record with a functional User Friendly UI to be able to view it. This is achieved using A host Computer, A Raspberry Pi with camera hardware as well as a webpage to display the attendance data The data is shared between Pi and the host laptop to manage the detected faces. The data gathering is done via a trained model through the host laptop and is used via the Pi camera to detect the faces and create the digital database. The impacts of the projects will be profound since it will save time and effort of skilled individuals as well as prevent the massive paper wastage that follows manual attendance monitoring. It will also help in ease of access in the attendance of data and allowing the users to monitor their attendance themselves. This also allows for storage of massive data for longer periods of time for future use and reusing the same systems later also conserving the costs and materials.

5G Converged Network Resource Allocation Strategy Based on Reinforcement Learning in Edge Cloud Computing Environment.

Aiming at the problem that computing power and resources of Mobile Edge Computing (MEC) servers are difficult to process long-period intensive task data, this study proposes a 5G converged network resource allocation strategy based on reinforcement learning in edge cloud computing environment. n order to solve the problem of insufficient local computing power, the proposed strategy offloads some tasks to the edge of network. Firstly, we build a multi-MEC server and multi-user mobile edge system, and design optimization objectives to minimize the average response time of system tasks and total energy consumption. Then, task offloading and resource allocation process is modeled as Markov decision process. Furthermore, the deep Q-network is used to find the optimal resource allocation scheme. Finally, the proposed strategy is analyzed experimentally based on TensorFlow learning framework. Experimental results show that when the number of users is 110, final energy consumption is about 2500 J, which effectively reduces task delay and improves the utilization of resources.

Application of multiscale magnetotelluric data to mineral exploration: an example from the east Tennant region, Northern Australia.

SUMMARYThe footprint of a mineral system is potentially detectable at a range of scales and lithospheric depths, reflecting the size and distribution of its components. Magnetotellurics is one of a few techniques that can provide multiscale data sets to image and understand mineral systems. We have used long-period data from the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP) as a first-order reconnaissance survey to resolve large-scale lithospheric architecture for mapping areas of mineral potential in northern Australia. The 3-D resistivity model reveals a broad conductivity anomaly extending from the Tennant Creek district to the Murphy Province in the lower crust and upper mantle, representing a potential fertile source region for mineral systems. Results from a higher-resolution infill magnetotelluric survey reveal two prominent conductors in an otherwise resistive host whose combined responses result in the lithospheric-scale conductivity anomaly mapped in the AusLAMP model. Integration of the conductivity structure with deep seismic reflection data reveals a favourable crustal architecture linking the lower, fertile source regions with potential depositional sites in the upper crust. The enhanced conductivity likely resulted from the remnant (metallic) material deposited when fluids were present during the ‘ancient’ tectonic events. This observation strongly suggests that the deep-penetrating major faults potentially acted as pathways for transporting metalliferous fluids to the upper crust where they could form mineral deposits. This result and its integration with other geophysical and geochronological data sets suggest high prospectivity for major mineral deposits in the vicinity of these major faults, that is, Gulunguru Fault and Lamb Fault. In addition to these insights, interpretation of high-frequency magnetotelluric data acquired during the infill survey helps to characterize cover and assist with selecting targets for stratigraphic drilling which, in turn, can validate the models and improve our understanding of basement geology, cover sequences and mineral potential. This study demonstrates that integration of geophysical data from multiscale surveys is an effective approach to scale reduction during mineral exploration in covered terranes with limited geological knowledge.

Empirical Map-Based Nonergodic Models of Site Response in the Greater Los Angeles Area

ABSTRACTWe develop empirical estimates of site response at seismic stations in the Los Angeles area using recorded ground motions from 414 M 3–7.3 earthquakes in southern California. The data are from a combination of the Next Generation Attenuation-West2 project, the 2019 Ridgecrest earthquakes, and about 10,000 newly processed records. We estimate site response using an iterative mixed-effects residuals partitioning approach, accounting for azimuthal variations in anelastic attenuation and potential bias due to spatial clusters of colocated earthquakes. This process yields site response for peak ground acceleration, peak ground velocity, and pseudospectral acceleration relative to a 760 m/s shear-wave velocity (VS) reference condition. We employ regression kriging to generate a spatially continuous site response model, using the linear site and basin terms from Boore et al. (2014) as the background model, which depend on VS30 and depth to the 1 km/s VS isosurface. This is different from past approaches to nonergodic models, in which spatially varying coefficients are regressed. We validate the model using stations in the Community Seismic Network (CSN) that are in the middle of our model spatial domain but were not considered in model development, finding strong agreement between the interpolated model and CSN data for long periods. Our model could be implemented in regional seismic hazard analyses, which would lead to improvements especially at long return periods. Our site response model also has potential to improve both ground-motion accuracy and warning times for the U.S. Geological Survey ShakeAlert earthquake early warning (EEW) system. For a point-source EEW simulation of the 1994 M 6.7 Northridge earthquake, our model produces ground motions more consistent with the ground-truth ShakeMap and would alert areas with high population density such as downtown Los Angeles at lower estimated magnitudes (i.e., sooner) than an ergodic model for a modified Mercalli intensity 4.5 alerting threshold.

Incremental Learning Method for Data with Delayed Labels

Most research on machine learning tasks relies on the availability of true labels immediately after making a prediction. However, in many cases, the ground truth labels become available with a non-negligible delay. In general, delayed labels create two problems. First, labelled data is insufficient because the label for each data chunk will be obtained multiple times. Second, there remains a problem of concept drift due to the long period of data. In this work, we propose a novel incremental ensemble learning when delayed labels occur. First, we build a sliding time window to preserve the historical data. Then we train an adaptive classifier by labelled data in the sliding time window. It is worth noting that we improve the TrAdaBoost to expand the data of the latest moment when building an adaptive classifier. It can correctly distinguish the wrong types of source domain sample classification. Finally, we integrate the various classifiers to make predictions. We apply our algorithms to synthetic and real credit scoring datasets. The experiment results indicate our algorithms have superiority in delayed labelling setting.

CNN-LSTM을 사용한 긴 주기 펄스 레이다 신호 속성 분류

In this paper, we propose a classification method of pulsed radar signal attributes by applying machine learning to a convolutional neural network and a long short-term memory(CNN-LSTM) network when the radio frequency and pulse repetition interval of the signal changes with a long period. CNN has a function to extract data features, and LSTM shows good classification performance when sequential data are highly correlated. However, LSTM has a problem that performance is degraded when the length of the input data is long. In the proposed method, CNN is applied to extract compressed features from long-period data to reduce the length of inputs to LSTM. In simulation results, the proposed method shows more than 95% correct classification rate when the data drop rate is 10% for 182,516 total radar signal attribute combinations.

Implementation of a Radon Monitoring Network in a Seismic Area

Large-scale radon monitoring is carried out due to the fact that it is directly responsible for public health. European Directive 2013/59/EURATOM has been transposed into the legislation of several countries and provides for the need for long-term monitoring of radon in homes and workplaces by setting the average annual reference level at 300 Bq/m3. At the same time, radon is a precursor factor, its emission being correlated with seismic and volcanic activity. In this case, the protection of the population is ensured by a forecast similar to a meteorological one. The NIEP (National Institute for Earth Physics) is developing a multidisciplinary real-time monitoring network in the most dangerous seismic area in Romania, Vrancea. This is located at the bend of the Carpathian Mountains and is characterized by deep earthquakes (over 80 km), with destructive effects over large distances. Implementing a multidisciplinary monitoring network that includes radon, involves finding the locations and equipment that will give the best results. There is no generic solution for achieving this, because the geological structure depends on the monitoring area, and in most cases the equipment does not offer the ability to transmit data in real time. The positioning of the monitoring stations was based on fault maps of the Vrancea area. Depending on the results, some of the locations were changed in pursuit of a correlation with zonal seismicity. Through repeated tests, we established the optimal sampling rate for minimizing errors, maintaining measurement accuracy, and ensuring the detection of anomalies in real time. The radon 222Rn was determined by the number of counts and ROI1 (region of interest) values, depending on the particularities of the equipment. Finally, we managed to establish a real-time radon monitoring network which transmits data to geophysical platforms and makes correlations with the seismicity in the Vrancea area. The equipment, designed to store data for long periods of time then manually download it with manufacturers’ applications, now works in real time, after we implemented software designed specifically for this purpose.

Lower crustal resistivity signature of an orogenic gold system

Orogenic gold deposits provide a significant source of the world’s gold and form along faults over a wide range of crustal depths spanning sub-greenschist to granulite grade faces, but the source depths of the gold remains poorly understood. In this paper we compiled thirty years of long-period magnetotelluric (MT) and geomagnetic depth sounding (GDS) data across western Victoria and south-eastern South Australia that have sensitivity to the electrical resistivity of the crust and mantle, which in turn depend on past thermal and fluid processes. This region contains one of the world’s foremost and largest Phanerozoic (440 Ma) orogenic gold provinces that has produced 2% of historic worldwide gold production. Three-dimensional inversion of the long-period MT and GDS data shows a remarkable correlation between orogenic gold deposits with > 1 t production and a < 20 Ω m low-resistivity region at crustal depths > 20 km. This low-resistivity region is consistent with seismically-imaged tectonically thickened marine sediments in the Lachlan Orogen that contain organic carbon (C), sulphides such as pyrite (FeS2) and colloidal gold (Au). Additional heat sources at 440 Ma due to slab break-off after subduction have been suggested to rapidly increase the temperature of the marine sediments at mid to lower crustal depth, releasing HS− ligands for Au, and CO2. We argue that the low electrical resistivity signature of the lower crust we see today is from a combination of flake graphite produced in situ from the amphibolite grade metamorphism of organic-carbon in the marine sediments, and precipitated graphite through retrograde hydration reactions of CO2 released during the rapid heating of the sediments. Thus, these geophysical data image a fossil source and pathway zone for one of the world’s richest orogenic gold provinces.

Assessment of Spatiotemporal Variability of Meteorological Droughts in Northern Iraq Using Satellite Rainfall Data

The absence of a dense rainfall monitoring network and longer period data are the major hindrances of hydroclimatic study in arid and semi-arid regions. An attempt has been made for the evaluation of spatiotemporal changes in droughts at the northern semi-arid region of Iraq for the period 1981–2018 using high-resolution (0.05°) precipitation data of Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). The performance of CHIRPS in replicating rainfall and Standard Precipitation Index (SPI) for different timescales at eleven locations for the available period of observation data (2000–2014) was evaluated. The SPI was also used to estimate drought frequency and evaluate drought trends at all the CHIRPS grid points. A modified version of the non-parametric Mann-Kendall (MK) test was employed for a robust evaluation of the spatial distribution of temporal trends in droughts. The results showed a good ability of CHIRPS in reconstructing observed SPI with a correlation coefficient ranged from 0.64 to 0.87, BIAS between 1.05 and 1.81, Nash-Sutcliff efficiency from 0.39 to 0.55, and Willmott Index between 0.67 and 0.79. The CHIRPS also able to reconstruct the time series and probability distribution of observed SPI reasonably. Spatial distribution of droughts revealed a higher frequency of droughts of all categories and timescales in the east and north of Northern Iraq, mainly due to high rainfall variance. The MK test revealed a reduction in 6- and 12-month droughts in the northwest and an intensification at a few northeastern grids. It indicates droughts became more recurrent in the already drought-prone region and lessened in a less drought-prone region.

Actual problems of the full-scale digital twins technology for the complex engineering object life cycle management

This article discusses the problems that arise with the widespread digital twin technology introduction for complex engineering objects. Based on the nuclear power experience, various classes of problems are considered. They are related to the different information models and data demand structures and for life-cycle at different stages. As part of the industrial technologies transformation during the Fourth Industrial Revolution, information model & digital twin concepts have fundamental importance. Target effects and actual problems of the technology are analyzed. The enlarged stages of the life cycle, such as design, construction, operation, and decommissioning are considered. The focus of the technology analysis is selected large objects of the Rosatom Corporation. Possible trends in the full life cycle digital twins technology are considered development. The problems of preserving large volumes of structured digital data for long periods at many decades are discussed. The problem of changing the competencies of engineering and management personnel in connection with the digital transformation of the economy and industry during the Fourth Industrial Revolution is considered. It is predicted that it is necessary to increase the costs of implementing digital technologies to achieve the target effects of the digital transformation of the industry and economy.