Series Of Products Research Articles

Mapping urban morphology changes in the last two decades based on local climate zone scheme: A case study of three major urban agglomerations in China

Urbanization has been bringing extensive land use and land cover change over the last several decades, which has an impact on the urban climate and also further affects public health and energy consumption, due to human activities. The local climate zone (LCZ) classification system was proposed in 2012 to depict the complexity of urban morphology. However, until the present, LCZ long-time series product was rarely seen for its mapping difficulty. This study proposed a framework for mapping annual LCZ time series with spatial-temporal consistency in the three major urban agglomerations in China and the temporal and spatial pattern of LCZ time series was also revealed. The result showed that the high-rise and open urban LCZ types tended to occupy higher proportion in the urban area in the past two decades and urban morphology varied much in urban expansion areas and urban renewal areas. From 2000 to 2020, the differences in urban morphology between the three urban agglomerations and urban areas of different urban sizes narrowed, but the differences between urban areas with different land uses widened.

Analysis of Instagram User Reception Against the Use of Korean Actors as Local Skincare Brand Ambassadors @somethincofficial

The Korean wave phenomenon in Indonesia is marked by the increasing number of people adapting Korean cultures such as food, fashion, and language. Until more and more local Indonesian products use BA K-pop, one of which is skincare brand Somethinc. When examined further, there is a contradiction between the skin color characteristics of Korean actors and the skin color of the majority of Indonesians. This study aims to find out how Instagram users interpret advertisements featuring Han So Hee promoting brightening series products. By using qualitative methods and interpretive paradigms through a reception analysis method approach. This study use interview as data collection techniques. The results of interviews with informants were then grouped into 3 (three) categories, namely Dominant Hegemonic, Negotiated, or Oppositional Position. This study shows the results from 6 sources that there are 2 people included in the Dominant Hegemonic category, then 1 person in the Negotiated category, and 3 people in the Oppositional Position category.

Interrelations of vegetation growth and water scarcity in Iran revealed by satellite time series

Iran has experienced a drastic increase in water scarcity in the last decades. The main driver has been the substantial unsustainable water consumption of the agricultural sector. This study quantifies the spatiotemporal dynamics of Iran’s hydrometeorological water availability, land cover, and vegetation growth and evaluates their interrelations with a special focus on agricultural vegetation developments. It analyzes globally available reanalysis climate data and satellite time series data and products, allowing a country-wide investigation of recent 20+ years at detailed spatial and temporal scales. The results reveal a wide-spread agricultural expansion (27,000 km^2) and a significant cultivation intensification (48,000 km^2). At the same time, we observe a substantial decline in total water storage that is not represented by a decrease of meteorological water input, confirming an unsustainable use of groundwater mainly for agricultural irrigation. As consequence of water scarcity, we identify agricultural areas with a loss or reduction of vegetation growth (10,000 km^2), especially in irrigated agricultural areas under (hyper-)arid conditions. In Iran’s natural biomes, the results show declining trends in vegetation growth and land cover degradation from sparse vegetation to barren land in 40,000 km^2, mainly along the western plains and foothills of the Zagros Mountains, and at the same time wide-spread greening trends, particularly in regions of higher altitudes. Overall, the findings provide detailed insights in vegetation-related causes and consequences of Iran’s anthropogenic drought and can support sustainable management plans for Iran or other semi-arid regions worldwide, often facing similar conditions.

Pelatihan aplikasi digital pengolahan AUM seri-PTSDL berbasis website bagi guru bimbingan dan konseling SLTA di Sumatera Barat

The rapid development of technology demands that all activities be carried out effectively and efficiently, as well as in the guidance and counseling assessment process, innovation and technology development is needed through the website-based AUM PTSDL series. The purpose of this service is training and developing school counselor skills in using the website-based AUM PTSDL assessment tool. This service is carried out by school counselors who are spread across districts in West Sumatra Province. Data was collected through an online questionnaire using Google From which was then analyzed using JAPS. Based on the results of the school counselor training, they felt very satisfied with the use of processing, appearance, ease of access, how to use the website-based PTSDL series AUM, then participants also felt that the material and practices presented by the resource person as a whole were very relevant. In the future, the website-based AUM PTSDL series product can be improved according to the advice of the school counselor and the cooperation of BK organizations is needed in socializing and promoting the product more broadly and the AUM PTSDL series can be developed with other formats.

Enabling laser transmission welding of additively manufactured thermoplastic parts using an expert system based on neural networks

In order to use laser transmission welding (LTW) for additively manufactured parts such as prototypes, small series, or one-off products, an enhanced process knowledge is needed to overcome the difficulties in the part composition resulting from the additive manufacturing process itself. In comparison to an injection molding process for thermoplastic parts, the additive manufacturing process fused deposition modeling leads to an inhomogeneous structure with trapped air inside the volume. In this paper, a neural network-based expert system is presented that provides the user with process knowledge in order to improve the weld seam quality of laser welded additively manufactured parts. Both additive manufacturing and LTW process are assisted by the expert system. First, the designed expert system supports the user in setting up the additive manufacturing process to increase the transmissivity. During welding, the additive manufacturing and LTW process parameters are used to predict the weld seam strength. To create the database for the expert system, specimens of transparent and black polylactide are additively manufactured. In order to change the transmissivity at an emission wavelength of 940 nm of the diode laser used, the manufacturing parameters for the transparent parts are varied. The transmissivity of the parts is measured with a spectroscope. The transparent samples are welded to the black samples with laser powers between 8 and 14 W in the overlap configuration and shear tensile tests are performed. In this work, the predictions of the transmissivity and the shear tensile force are demonstrated with an accuracy of more than 88.1% of the neural networks used for the expert system.

Reveal the severe spatial and temporal patterns of abandoned cropland in China over the past 30 years

The abandonment phenomenon is familiar in China. It threatens food security and seriously affects the sustainable development of society, the economy, and the natural environment. However, monitoring and mapping abandoned cropland on a large scale remains a challenge because of its complex land-use change process. According to the World Food and Agriculture Organization, cropland not used for agricultural production for more than 5 years is defined as abandoned cropland. This study uses the Landsat high-precision long-time series product to detect cropland nationwide from 1990 to 2019 by using the sliding window method on Google Earth Engine to profile the spatial distribution, intensity, trend, frequency, and recultivation of abandoned cropland. Between 1992 and 2015, the results illustrate that the total area of abandoned cropland in China was 559,170.26 km2, 18.59 % of the cropland area. Excluding the recultivated cropland area, China's abandoned cropland area amounted to 392,156.24 km2, 13.03 % of the total cropland. Cropland abandonment is mainly distributed in the second terrain gradient in midwestern and southwestern regions such as Inner Mongolia and Gansu. It rarely happens in western and eastern coastal areas. A high abandonment rate area usually has high elevation and slope. The light index is negatively correlated with the abandonment rate in suburban areas. This study is the first to map the spatiotemporal distribution of abandoned cropland in China with high precision at 30 m resolution. It provides an important basis for policies regarding the recultivation of abandoned cropland.

Recent trends of land surface temperature in relation to the influencing factors using Google Earth Engine platform and time series products in megacities of India

Increasing land surface temperature (LST) is one of the significant anthropogenic issues that is threatening livelihoods in urban areas. Understanding the urban LST is essential for sustainable management of the urban landscape. Various studies focused on the spatial pattern and average trend of LST in relation to influencing factors, but little is known regarding the pixel-scale spatiotemporal trends and integration of multiple influencing factors in the different seasons using time series data. Therefore, we focused on the spatiotemporal trends of LST in relation to the influencing factors using the Google Earth Engine platform and time series products. MODIS land surface temperature data was used to estimate the annual and seasonal day-night LST from 2003 to 2020. Theil-Sen slope and Mann-Kendall statistics were used to examine the magnitude and significance of the spatiotemporal trend. All-subsets regression and hierarchical partitioning (HP) were applied to explore the driving factors of LST. Average LST and range varied between the cities, but were highest in Chennai and Delhi, respectively. Except for winter nighttime in Kolkata, all cities' annual and seasonal nighttime LST are increasing (0.027–0.075 °C/year) over a large part of the study area (33.934%–100% of areas), indicating that anthropogenic force is increasing in urban areas during nighttime. Annual and seasonal daytime LST is increasing in most parts (55.545%–94.604% of areas) of Chennai and Kolkata, but daytime LST is decreasing (33.602%–98.571% of areas) in Mumbai and Delhi due to the increasing concentration of absorbing aerosol over the city. Strength of independent factors and multiple influencing factors varied in the day-night time between the cities. But nighttime LST was well explained using the all-subsets regression compared to daytime LST, except for Mumbai. Also, nighttime light density and NDBI had consistent effects on nighttime, and daytime, respectively. These findings can help to take scientific adaptation and mitigation strategies for sustainable development.

TECHNO-ECONOMIC ANALYSIS OF THE INNOVATED TECHNOLOGY FOR THE PRODUCTION OF WAGONS BRAKE TRIANGLE

Increasing profits by reducing costs during product development and its production and quality increasingare challenges that companies face every day in their operations. The aspiration of modern production is to achievean effect similar to those in mass production (or at least in large series production) even on extremely small series ofproducts, even on personalized products. Techno-economic analysis in modern companies is an integral part of thecompany's modern information system. This paper presents a techno-economic analysis of innovative technologiesfor the production of a wagons triangle brake, a product that is an integral part of the brake system of freight andpassenger wagons of the older generation: casting and additive technologies. Given that there are currently availabledifferent production technologies that can be applied, one of the criteria for choosing a production technology is itsprofitability. The choice of solution, whether the product will be made by casting (or welding) or additivetechnology, is mostly influenced by the size of the series. In the case of the cast part, the so-called constant costs(model, molds for cores), but variable costs are therefore lower, i.e., costs proportional to the number ofmanufactured pieces. Additive technologies that can be used to make such parts can use numerous methods, three ofwhich stand out due to their application in practice. These are stereolithography (STL), extruded material (EBM)and direct metal laser sintering (DMLS). Machine cost is the main factor for stereolithography and melt extrusion,while material cost is the main factor for laser sintering. The price of additively manufactured parts is calculatedaccording to the cost model that follows activities, where each cost is linked to a specific activity. The analysisindicates that additive technologies are not such a cheap process, especially in serial production, but they areexcellent in individual production or during rapid prototyping, and the price of the product obtained by additivetechnology is the same for each piece. Also, cast parts have high initial costs (casting molds, etc.), but castings arerelatively cheap, which leads to a drop in the total price as the series increases. Both technologies have limitations interms of the materials that can be used in casting or additive manufacturing processes. Based on the technoeconomicanalysis, it can be concluded, also, that certain activities require the engagement of other companies thatshould be entrusted with certain business activities or tasks - in this way, the company's resources are saved andemployed on other more expedient activities that will bring greater benefits, and the system work is not disturbed ordisturbed. The advantage of casting technology over additive technologies is already profitable for series over 42pieces, and compared to forging, the casting of this part of the product is 2.5 times cheaper.

The Impact of the Collaboration Between UNIQLO and Japanese Anime/Manga for its UT Collection Towards Brand Loyalty in Indonesia

Fast fashion brands are encouraged to keep innovating new brand experiences to stay competitive in the market. UNIQLO is one of the four major players in the worldwide fast fashion industry. However, UNIQLO is still inferior to its two major competitors. In 2013, UNIQLO started to provide new brand experiences — its main competitors do not have — through its self-expression graphic print collections, the UT Series. Focusing on Generation Z with the characteristics of being disloyal to brands and desired for freedom of expression, this research aims to identify the perceived brand experience from UT Series that specifically collaborated with Japanese anime/manga and its effectiveness in affecting UNIQLO brand loyalty. This research is conducted using a qualitative approach through semi-structured interviews and a quantitative approach through an online survey with UNIQLO Generation Z customers who have purchased at least 1 UT Series product that collaborates with Japanese anime/manga in the past 1 year as the respondents. The interview results are analyzed using the open coding method and the survey results are analyzed using the PLS-SEM method. The results show that the brand experience of UT Series that collaborates with Japanese anime/manga positively influences UNIQLO brand loyalty with brand love, brand trust, and brand prestige as mediating variables. The findings will give insight into the effectiveness of brand experience in affecting brand loyalty to increase sales.

A Fully Unsupervised Machine Learning Framework for Algal Bloom Forecasting in Inland Waters Using MODIS Time Series and Climatic Products

Progressively monitoring water quality is crucial, as aquatic contaminants can pose risks to human health and other organisms. Machine learning can support the development of new effective tools for water monitoring, including the detection of algal blooms from remotely sensed image series. Therefore, in this paper, we introduce the Algal Bloom Forecast (ABF) framework, a fully automated framework for algal bloom prediction in inland water bodies. Our approach combines machine learning, time series of remotely sensed products (i.e., Moderate-Resolution Imaging Spectroradiometer (MODIS) images), environmental data and spectral indices to build anomaly detection models that can predict the occurrence of algal bloom events in the posterior period. Our assessments focused on the application of the ABF framework equipped with the support vector machine (SVM), random forest (RF), and long short-term memory (LSTM) methods, the outcomes of which were compared through different evaluation metrics such as global accuracy, the kappa coefficient, F1-Score and R2-Score. Case studies covering the Erie (USA), Chilika (India) and Taihu (China) lakes are presented to demonstrate the effectiveness and flexibility of our learning approach. Based on comprehensive experimental tests, we found that the best algal bloom predictions were achieved by bringing together the ABF design with the RF model.

Photosynthesis phenology, as defined by solar-induced chlorophyll fluorescence, is overestimated by vegetation indices in the extratropical Northern Hemisphere

Vegetation phenology is highly sensitive to climate change, although the data and methods used to estimate key phenological states can influence this sensitivity. Because of its direct relation to leaf photosynthetic carbon uptake, remotely sensed solar-induced chlorophyll fluorescence (SIF) can provide new insight assessing changes in vegetation phenology. Here, we investigated the potential of using a SIF time series product named contiguous SIF (CSIF) to estimate spring, summer, and autumn phenology in the extratropical Northern Hemisphere (>30°N) and compared the results with those based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) for the period 2001–2018. Overall, we found similar spatial patterns in phenological states. However, specific dates of key phenological events differed when using CSIF vs. MODIS NDVI data. NDVI data indicated that the growing season started earlier (by 10.1 days on average) and ended later (11.5 days on average) relative to CSIF data. This implies that actual periods for photosynthetic activity are shorter (by 21.6 days on average) than those estimated from vegetation indices more directly related to changes in canopy structure. These large differences between results from NDVI and that from CSIF suggest that vegetation indices such as NDVI seem to overestimate the period for active photosynthesis over the extratropical Northern Hemisphere. Furthermore, while phenology of the early growing season is dominated by temperature for both NDVI and CSIF data, phenology of the late growing season is mainly controlled by temperature for NDVI but by precipitation for CSIF. Our findings were further confirmed by other SIF (GOME-2 SIF) and vegetation index (MODIS EVI) datasets. Phenology modes in Earth system modelling are often parameterized using leaf unfolding and senescence from either station or satellite observations. Our results imply that canopy structure-based parameterization schemes may have overestimated photosynthesis active period, and thus productivity responses. We conclude that SIF data offers a novel and unique approach for assessing phenological change - one that is more directly tied to the carbon cycle and how it is being influenced by climate change.

An Approach for Retrieving Consistent Time Series “Urban Core–Suburban-Rural” (USR) Structure Using Nighttime Light Data from DMSP/OLS and NPP/VIIRS

The long time series and consistent “urban core-suburban-rural” (USR) structure in a city region is essential to understanding urban–suburban–rural interaction and urbanization pathways. It is always considered to be a single land use type (e.g., impervious area) in remote sensing research. The long-term (1992–present) nighttime light (NTL) data of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) provide the potential for retrieving time series of USR structure. In this study, we propose an improved approach to mapping the USR structure of the three subcategories based on a heuristic algorithm of Mann–Kendall mutation detection on the NTL quantile curve. First, a minor adjustment of VIIRS NTL is applied for matching the value ranges of DMSP NTL data and keeping the advantage of VIIRS to generate a long-term NTL dataset. Second, the heuristic algorithm of Mann–Kendall mutation detection is processed to find two optimal thresholds in the NTL quantile curve, which is used for USR extraction. Finally, a temporal consistency check is used to post-process the initial USR area for obtaining a more consistent and reliable USR sequence. To evaluate the performance of the proposed method, we retrieved the USR structures of 19 typical cities in China from 1992 to 2020 based on NTL datasets. The evaluations of spatiotemporal consistency compared with the validation data indicate that the USR retrieval results show good agreement with the land use map derived from Landsat images and the time series product from MODIS. The average overall accuracy (OA) of overall urban extent is higher than 0.95 and the average kappa coefficient (KC) reaches 0.6. Moreover, we investigated the urban dynamics and USR interactions of 19 cities from 1992 to 2020. Overall, this study proposes an improved approach for long-term USR mapping from NTL images at a regional scale and it will provide a valuable method for urbanization dynamics analysis.

Spatial Modelling and Prediction with the Spatio-Temporal Matrix: A Study on Predicting Future Settlement Growth

In the past decades, various Earth observation-based time series products have emerged, which have enabled studies and analysis of global change processes. Besides their contribution to understanding past processes, time series datasets hold enormous potential for predictive modeling and thereby meet the demands of decision makers on future scenarios. In order to further exploit these data, a novel pixel-based approach has been introduced, which is the spatio-temporal matrix (STM). The approach integrates the historical characteristics of a specific land cover at a high temporal frequency in order to interpret the spatial and temporal information for the neighborhood of a given target pixel. The provided information can be exploited with common predictive models and algorithms. In this study, this approach was utilized and evaluated for the prediction of future urban/built-settlement growth. Random forest and multi-layer perceptron were employed for the prediction. The tests have been carried out with training strategies based on a one-year and a ten-year time span for the urban agglomerations of Surat (India), Ho-Chi-Minh City (Vietnam), and Abidjan (Ivory Coast). The slope, land use, exclusion, urban, transportation, hillshade (SLEUTH) model was selected as a baseline indicator for the performance evaluation. The statistical results from the receiver operating characteristic curve (ROC) demonstrate a good ability of the STM to facilitate the prediction of future settlement growth and its transferability to different cities, with area under the curve (AUC) values greater than 0.85. Compared with SLEUTH, the STM-based model achieved higher AUC in all of the test cases, while being independent of the additional datasets for the restricted and the preferential development areas.

A new European service to share GNSS Data and Products

This paper describes the new GNSS data and product services that have been developed within the context of the EPOS (European Plate Observing System) European Research Infrastructure Consortium (ERIC), which is part of the European Strategy Forum on Research Infrastructures. These services, optimized for Solid Earth research applications, endeavour to harmonise, and standardise Global Navigation Satellite System (GNSS) data collection and processing. They have been implemented by the members of the GNSS Data & Products (EPOS-GNSS), one of the Thematic Core Services (TCS) of EPOS with the active support of national and pan-European infrastructures (in particular the Regional Reference Frame Sub-Commission for Europe (EUREF) of the International Association of Geodesy). The optimized use of data from dozens of diverse European GNSS networks, installed not specifically for geodynamic studies, created additional requirements from an organizational and technical point of view, the solutions for which we describe in this article. The data flows from data suppliers and analysis centers to the various TCS Data & Product Portals are described, as well as their integration into the overall EPOS system. This is made through GLASS (GNSS Linkage Advanced Software System), a dedicated software package developed since 2016, whose architecture and functionalities are detailed here. Time series and other GNSS products computed at the several analysis centers are described as are the quality control steps that are performed. Finally, several user cases are presented that demonstrate how different stakeholders (from data providers to scientists) can benefit from the efforts being carried out by the EPOS- GNSS community.

DIFFERENTIAL SAR INTERFEROMETRY FOR THE MONITORING OF LAND SUBSIDENCE ALONG RAILWAY INFRASTRUCTURES

Abstract. The paper summarizes the results of an ongoing research project carried out in cooperation between CTTC (Spain) and the University of Milan (Italy). The work aimed at investigating the role of quality indicators in the analysis of differential interferometric SAR time series products. Small baseline multi-temporal differential interferometric techniques have been used to derive TS products from six-year Sentinel-1 images covering railway networks in Barcelona, Spain. Redundancies of interferograms and post-phase unwrapping phase estimation residuals were pivotal parameters in determining the reliabilities of measurements. Preliminary results have supported the importance of quality indicators as well as the feasibility of multi-temporal differential interferometric techniques in monitoring subsidence along railway infrastructures. The time series evolutions of measurements from coherent scatterers have also shown that the target area is stable in the study period.

Continuity of Formal Power Series Products in Nonlinear Control Theory

Formal power series products appear in nonlinear control theory when systems modeled by Chen–Fliess series are interconnected to form new systems. In fields like adaptive control and learning systems, the coefficients of these formal power series are estimated sequentially with real-time data. The main goal is to prove the continuity and analyticity of such products with respect to several natural (locally convex) topologies on spaces of locally convergent formal power series in order to establish foundational properties behind these technologies. In addition, it is shown that a transformation group central to describing the output feedback connection is in fact an analytic Lie group in this setting with certain regularity properties.

Big data collection in pharmaceutical manufacturing and its use for product quality predictions

Advances in data science and digitalization are transforming the world, and the pharmaceutical industry is no exception. Multiple sensor-equipped manufacturing processes and laboratory analysis are the main sources of primary data, which have been utilized for the presented dataset of 1005 actual production batches of selected medicine. This dataset includes incoming raw material quality results, compression process time series and final product quality results for the selected product. The data is highly valuable for it provides an insight into every 10 seconds of the process trajectory for 1005 actual production batches along with product quality collected over several years. It therefore offers an opportunity to develop advanced analysis models and procedures which would lead to the omission of current conventional and time consuming laboratory testing. Benefits for both the industry and patient are obvious: reducing product lead times and costs of manufacture.

Optimally integrating multi-source products for improving long series precipitation precision by using machine learning methods

Accurate estimation of precipitation over the Qinghai-Tibet Plateau (TP) remains a challenge at high spatio-temporal scales. This research proposed a machine learning (ML) framework based on multi-source products which was involved with seasonal precipitation characteristics and terrain factors, to improve the spatio-temporal accuracy of precipitation and hydrology simulations in the Yalong River Basin on the southeastern edge of the TP. The main conclusions and results include but were not limited to: (1) The ML model based on terrain factors and seasonal precipitation characteristics can wisely integrate multi-precision and multi-source precipitation products of different resolutions and different time scales, indicating that the merge model is robust and has a good generalization ability; (2) Based on measured gauges, the proposed merge model improved the accuracy of precipitation products. The Pearson's correlation coefficient (R) increased from 0.52 to 0.84, and Root mean square error (RMSE) had been compressed from 5.5 mm/day to 1.99 mm/day, and the spatial distribution of precipitation in regions with few measured gauges was corrected; (3) The accuracy of long series product obtained by merge model can be used to evaluate from the perspective of hydrological simulation. The method proposed in this study may be helpful to greatly improve the temporal and spatial accuracy of precipitation in high latitude areas with complex terrain and was committed to the study of hydrological cycle.

Generating continuous fine-scale land cover mapping by edge-guided maximum a posteriori based spatiotemporal sub-pixel mapping

Global land cover mapping activities are of great important for retrieving the environment we are living in. However, the trade-off between spatial and temporal resolution makes it difficult to obtain the continuous fine-scale land cover product for detail and frequent land surface analysis. To overcome the difficulty, this study proposed a modified maximum a posteriori (MAP) based spatiotemporal sub-pixel mapping (SSM), for a long-term and fine-scale land cover mapping. The proposed approach consists of three parts, i.e., data fidelity term, spatial regularization term, and enhanced temporal regularization term, which can make use of historical fine-scale image for a better super-resolution of the current coarse image. One regional experiment was implemented for the algorithm validation, and one real experiment was investigated, by generating continuous land cover mapping products (2010–2015) at Landsat-like spatial resolution from time series of MODIS images. Compared with traditional methods, the proposed approach reconstructed more pleasant spatial details and had greater quantitative accuracy, outperforming the state-of-the-art spatiotemporal sub-pixel mapping method by averagely 2.58% for all the inspected dates in OA metric. This study not only provides a generalized research framework for generating continuous fine-scale land cover product from daily available MODIS images, but also validates the usability of the time series products for detail land cover monitoring, and reveals a finding that the study area of Wuhan is a well practitioner of the sustainable development policy during its urbanization process.

"Episode — series — series products" as structural components of modern culture

It is argued that from decade to decade Ukrainian humanities are actively expanding the research space, discovering new, theoretically important "problem areas", the understanding of which may have prospects by using the potential of cultural analysis, including factors such as "interdisciplinarity", "personalization" and "dialogue". These include a range of issues related to the specifics of the "series". It is noted that due to the involvement of the concept of "paradigm" in the analysis of the situation in the field of art, the facts of partial or complete change, especially patterns of thinking in motion from film to TV series and from cinema to television. It is stated that the interest in television in general and the "television series" in particular intensified during the '70s and '90s. This was due to a number of reasons, including the rapid entry into the space of humanitarian knowledge of cultural studies. As a unity of history and theory of culture, it prompted a revision of the research orientation of the traditional humanities. It is noted that the ability to record the transformation of "film — series" encourages the use of art history dimension of culturology — a science that represents a combination of three components: theory, history and art criticism. At the same time, the history of art is seen as a procedural phenomenon, as it accumulated gradually: art forms were formed not simultaneously but over the centuries, entrenched in the dynamic movement of artistic development of reality.