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Structural analysis of regional economies plays a significant role in political decision-making. In the context of Russian regional development, a new research topic emerges at the level of economic macroregions as defined in the current Spatial Development Strategy of the Russian Federation. The present work aims to empirically evaluate the use of shift-share analysis to clarify the contribution of the neighbourhood effect in addition to national and regional growth factors. The proposed approach incorporates structural shifts of spatial interactions into the classical method using various matrices (adjacency, neighbourhood). Macroregional economic growth was decomposed on the basis of consolidated sectoral structural dynamics and national development stages from 2004–2018 to reveal specific features of structural growth. It was determined that the most significant effects at all stages are those generated by national economic policy. Regional effects are especially important for macroregions whose development is characterised by substantial state support combined with an adequate resource base. Negative values of the spatial sectoral shift persisting across all macroregions testify to the positive mutual influence of macroregional specialisation and intensified interregional cooperation. Sectoral effects were observed only for particular economic sectors or at certain macroregional development stages. Recommendations for strengthening interregional interactions are proposed on the basis of the enlarged typology of structural effects in macroregions. Comparative analysis confirmed the consistency of estimates obtained using standard and spatial shift-share methods. Future research will focus on clarifying the structure of spatial relationships and detailing actual interregional economic interactions in the form of products and goods exchanged between macroregions.

The study, through the framework of mobility and space, explores the phenomenon of multiple shifts in learning spaces induced by COVID-19. The Interpretative Phenomenological Approach (IPA) is adopted to document the experiences and perceptions of learners caught within these spatial shifts—physical, online, and hybrid. Online interviews were conducted with six first-year undergraduate and three first-year postgraduate students enrolled at the department of English and Cultural Studies in a Southern Indian University. Some of the dominant patterns emerging from the accounts of the participants are (1) the changing perception of conducive learning space, (2) the changing perceptions and roles of various classroom actors, and (3) the evolving nature of the learners and the learning process. The study utilizes the framework of mobility to locate the stage of embodied skill acquisition of the participants within the online learning space and illuminates the possibilities offered by this paradigm within the context of higher education. Some of the insights gained through the study include a changing perception of the conventional built classroom space, a notable preference towards a complete online or offline mode as opposed to the hybrid mode, and a transition towards self-directed learning. The study argues that these implications are highly pertinent and can significantly shape the way pedagogues and researchers engage with the various modes of learning—physical, online, and hybrid—and the future of higher education that is shaped by technology-enabled learning.

The spatial and temporal variability of ice edge position at the end of the summer period, the dates of the beginning of stable ice formation in early autumn in the Russia’s Eastern Arctic seas (Laptev, East-Siberian, and Chukchi) during 1981–2018 together with climate changes in the 21st century are analyzed. The analysis of summer and autumn ice characteristics shows certain common features occurring for many years: since the beginning of the 21st century, ice conditions have significantly improved, which is due to the more Northern position of the ice border in August–September and later dates for the beginning of stable ice formation in September–October. On average for the period 2002–2018, the ice edge at the end of the period of clearance from ice shifted (as compared to 1981–2001) in a northerly direction by about 400–500 km. Observations did show that in 2003–2018, the beginning of stable ice formation in the autumn season (as compared to 1981–2002) occurred later by about three weeks. The maximum northward shift of the ice edge was first observed (since 2002) on the aquatory to the East of the New Siberian Islands, and then (since 2011) – to the West of them. The greatest anomalies of late dates (onset) of ice formation were first observed (since 2003) in the Chukchi Sea, then (since 2008) – in the Laptev Sea, and later (since 2014) – again in the Chukchi Sea. Long-term changes in the ice conditions occur according to the similar scenario: first, an anomaly of the latitudinal position of the edge or the date of the ice formation beginning was formed in the Eastern part of the studied aquatory, and then this anomaly shifted from East to West. At the same time, the anomaly diminishes in the East that makes it possible to interpret the observed natural changes as fluctuations of the «ice wave» type. Taking into account the wave features of changes in summer and autumn ice indicators, we can assume that in the 2020s we should expect a similar continuation of natural fluctuations in changes in ice conditions, which will be accompanied by a spatial shift of the ice edge in the South direction and relatively earlier dates for the beginning of ice formation.

The paper proposes a new version of spatial shift-share decomposition to improve on the various approaches to conventional shift-share analysis found in the literature. The novelty of our proposal is that it enables researchers to assess spatial competitiveness effects controlling for the influence of industrial specialization at both regional and neighborhood level. This new version is applied to inbound tourism in Italian regions and enables us to identify the best and worst performers. Our empirical results identify favorable scenarios in some areas of the country, such as Sardinia as well as regional advantage in a sizeable number of well-known destinations.

Captivating film posters and advertisements with images of idolized actors like Mammootty and Mohanlal blended in with vivid colours and appealing captions is a spectacle that is a part of the cult...

Aerosol particles can serve as cloud condensation nuclei (CCN) to influence orographic clouds. Autoconversion, which describes the initial formation of raindrops from the collision of cloud droplets, is an important process for aerosol-cloud-precipitation systems. In this study, seven autoconversion schemes are used to investigate the impact of CCN on orographic warm-phase clouds. As the initial cloud droplet concentration is increased from 100 cm−3 to 1000 cm−3 (to represent an increase in CCN), the cloud water increases and then the rainwater is suppressed due to a decrease in the autoconversion rate, leading to a spatial shift in surface precipitation. Intercomparison of the results from the autoconversion schemes show that the sensitivity of cloud water, rainwater, and surface precipitation to a change in the concentration of CCN is different from scheme to scheme. In particular, the decrease in orographic precipitation due to increasing CCN is found to range from −87% to −10% depending on the autoconversion scheme. Moreover, the surface precipitation distribution also changes significantly by scheme or CCN concentration, and the increase in the spillover (ratio of precipitation on the leeward side to total precipitation) induced by increased CCN ranges from 10% to 55% under different autoconversion schemes. The simulations suggest that autoconversion parameterization schemes should not be ignored in the interaction of aerosol and orographic cloud.

Deep neural networks (DNN) have been applied to numerous artificial-intelligence applications because of their remarkable accuracy. However, computational requirements for deep neural networks are recently skyrocketing far beyond the Moore’s Law. In addition to the importance of accuracy, the industry’s demand for efficiency in model learning process is increasing. This has led to various attempts to make DNNs more lightweight. Hence, we propose a modeling technique that applies lightweight convolutional neural networks (CNN) to handle the model-learning processes for DNNs. The proposed spatial-shift pointwise quantization (SSPQ) model elegantly combines compact network-design techniques to revitalize DNN quantization efficiency with little accuracy loss. We set the depths of our SSPQ model to 20, 34, and 50 to test against CIFAR10, CIFAR100, and ImageNet datasets, respectively. By applying SSPQ20 to the CIFAR10 dataset, we reduced accuracy degradation by 2.95%, while reducing the number of parameters $8.7\times $ . For the same dataset, our “wide” SSPQ20 variant reduced training parameters $1.96\times $ , compared with the ResNet20 model architecture, which provided a 0.2% improvement. By applying SSPQ34 to the CIFAR100 dataset, we successfully reduced the number of learning parameters $8\times $ , compared with the ResNet34 model with an accuracy degradation of 4.57%. By applying SSPQ50 to the ImageNet dataset, we successfully reduced the number of parameters $10.2\times $ over ResNet50 with an accuracy degradation of only 2.68%. Therefore, using only 9.78MB of learning parameter, the SSPQ50 model guarantees 73.6% accuracy. This is an improved performance by 1.46% in terms of accuracy and 3.62MB in terms of model size compared to the MobileNetV2 lightweight model. The source code is available at https://github.com/Eunhui-Kim/SSPQ .

This paper focuses on how to make a charging guidance for urgent charging electric vehicles (EVs) to fast charging stations. To avoid charging overload in the spatial-temporal scale and excessive waiting time for EV users, a charging guidance strategy based on the virtual service range is proposed. Considering the mutual influence among EVs' charging selections, the proposed strategy can unify the temporal shift and spatial shift of the charging load into the same time scale to control rather than other works only do the temporal shift. Specifically, the proposed strategy has two stages: the charging arrangement and the spatial-temporal shift. The charging arrangement stage is to determine whether an EV can be allowed to charge at a certain time and a given charging station while the spatial-temporal shift stage is to regulate the charging time or charging location. Besides, a bucketsort-based algorithm is proposed to solve the formulated problem in the charging arrangement stage. Finally, three scenarios have been designed to demonstrate the feasibility of the proposed strategy and the performance of the presented algorithm is compared with the other decentralized algorithms.

The mutual influence of interregional migrations, the housing market, and the spatial transformation of the Moscow metropolitan area are studied empirically using the database of housing development projects and the depersonalized database of the residence addresses of buyers in the primary housing market. We consider the influence of natural rent, agglomeration economy, interregional inequality, and distance on the differentiation of the activity of residents of various Russian regions and cities in the primary housing market of the Moscow metropolitan area compared to their migration activity. The main characteristics of the extensive development of the Moscow metropolitan area are the prevalence of large greenfield residential district projects, urban sprawl in the near suburban zone, and the concentration of construction along transport corridors in the middle suburban zone. Selective influence of buyers from other regions on various zones of the Moscow metropolitan area owes to their orientation at the most affordable proposals in the primary market. The near suburban zone is the main gate into the Moscow metropolitan area for migrants, and construction in this zone is a major mechanism of restricting housing prices and maintaining economic incentives for large‐scale migration to the Moscow region. Proceeding from the generalization of factors of the spatial transformation of the Moscow metropolitan area, a conceptual model of interaction between migration and urban spatial shifts is proposed. La influencia mutua de las migraciones interregionales, el mercado de la vivienda y la transformacion espacial del area metropolitana de Moscu se estudian empiricamente mediante una base de datos de proyectos de desarrollo de viviendas y una base de datos despersonalizada de los domicilios de compradores en el mercado primario de la vivienda. Se considero la influencia de la renta natural, la economia de aglomeracion, la desigualdad interregional y la distancia en la diferenciacion de la actividad de los residentes de diversas regiones y ciudades rusas en el mercado primario de la vivienda del area metropolitana de Moscu, en comparacion con su actividad migratoria. Las principales caracteristicas del extenso desarrollo del area metropolitana de Moscu son el predominio de grandes proyectos de distritos residenciales en zonas verdes, la dispersion urbana en la zona suburbana cercana, y la concentracion de la construccion a lo largo de corredores de transporte en la zona suburbana media. La influencia selectiva de los compradores de otras regiones en varias zonas del area metropolitana de Moscu se debe a su orientacion hacia las propuestas mas asequibles del mercado primario. La zona suburbana mas cercana es el principal portal de entrada al area metropolitana de Moscu para los migrantes, y la construccion en esta zona es un importante mecanismo de restriccion de los precios de la vivienda y para mantener los incentivos economicos de la migracion en gran escala a la region de Moscu. A partir de la generalizacion de los factores de transformacion espacial del area metropolitana de Moscu, se propone un modelo conceptual de interaccion entre la migracion y los cambios espaciales urbanos. モスクワ大都市圏における地域間移動、住宅市場および空間変容の相互影響を、住宅開発プロジェクトのデータベースおよび住宅の一次市場における購入者の住所の匿名加工情報データベースを用いて実証的研究を行った。ロシアの様々な地域における住民の活動とモスクワ大都市圏の住宅の一次市場における都市部の住民の活動の差別化に対して、自然レント、集積経済、地域間の不平等、距離が及ぼす影響を、彼らの移住活動と比較して検討した。モスクワ大都市圏における大規模開発の主な特徴は、大規模なグリーンフィールド住宅地区プロジェクトの普及、郊外に近いゾーンにおける都市のスプロール化、中間の郊外ゾーンにおける交通回廊に沿った建設地の集中である。モスクワ大都市圏の様々なゾーンに対する他の地域からの購入者の選択的な影響は、彼らが一次市場における最も手頃な提案価格を指向することによるものである。郊外に近いゾーンは、移住者にとってのモスクワ大都市圏へのメインゲートであり、このゾーンにおける建設は、住宅価格を制限し、モスクワ地域への大規模移住に対する経済的インセンティブを維持するための重要なメカニズムである。モスクワ大都市圏の空間変容の要因の一般化に続いて、移動と都市空間シフトとの相互作用の概念的モデルを提案する。

We live in a dynamic, distracting world. When distracting information captures attention, what are the consequences for perception? Previous literature has focused on effects such as reaction time (RT) slowing, accuracy decrements, and oculomotor capture by distractors. In the current study, we asked whether attentional capture by distractors can also more fundamentally alter target feature representations, and if so, whether participants are aware of such errors. Using a continuous report task and novel confidence range report paradigm, we discovered 2 types of feature-binding errors when a distractor was presented along with the target: First, when attention is strongly captured by the distractor, participants commit swapping errors (misreporting the color at the distractor location instead of the target color), which remarkably seem to occur without awareness. Second, when participants successfully resist capture, they tend to exhibit repulsion (perceptual distortion away from the color at the distractor location). Thus, we found that capture not only induces a spatial shift of attention, it also alters feature perception in striking ways. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Solar photovoltaic (PV) has become the fastest-growing new energy in China and one of the main contributors to China’s clean energy transition. From 2013 to 2019, China’s solar PV installed capacity grew from 15,890 MW to 204,180 MW, increasing by 11.85 times. To explore solar PV investment changes across China regions, we use spatial shift-share analysis model to decompose solar PV investment changes from 2013 to 2019 into four components: national energy investment growth effect (NEG), national energy investment structure effect (NES), neighbor–nation solar PV investment competitive effect (NNC), and region–neighbor solar PV investment competitive effect (RNC). Based on the decomposition results, we find that the value of NNC of most western provinces is negative for the entire period, while the NNC of most central and eastern provinces is in the middle and lower range. There is little difference in RNC among these regions. While comparing the influence caused by the four effects, NNC and RNC play dominant roles in solar PV investment changes in eastern and central provinces, which means NEG and NES have relatively small impacts. By contrast, NEG and NES affect the solar PV investment changes at a larger scale in most western provinces. Comparing the NNC and RNC, we find that RNC played a prominent role in the eastern and central regions, while NNC played a dominant role in the west.

Measuring image visual quality is extremely important for many image processing tasks. In the past, several metrics have been proposed for measuring image visual quality such as structural similarity index (SSIM) and visual information fidelity (VIF). Nevertheless, these metrics are not robust to image spatial shifts when the reference and distorted images are misaligned by a few pixels. These metrics generate extremely low metric scores which is undesirable. It is well known that shifting the image by a few pixels does not affect the perceived image quality significantly. In this paper, we modify the SSIM metric to make it more robust to spatial shifts by pre-processing the input images with two-dimensional (2D) Fast Fourier Transform (FFT2). We then use the magnitudes of the Fourier coefficients in the existing metrics since these coefficients are shift-invariant. Experiments show that our proposed novel method is particularly good at measuring the visual quality of 2D images because it is far less complex than the existing methods and it offers better accuracy. Our new method is better than SSIM even when no spatial shifts are introduced to the images.

Objectives To investigate spatial pattern data signatures in residential burglary resulting from the crime drop. Methods Geo-referenced crime data, 2003–2016, representing residential burglary are analyzed considering crime concentrations at the street segment level as well as through the use of a nonparametric spatial point pattern test that identifies the stability in spatial point patterns in pairwise and longitudinal contexts. Results There has been a statistically significant change in the spatial pattern of residential burglary. The decline and corresponding spatial shift in residential burglary from 2003 to 2016 is related to income and changes in urban form, particularly the use of particular kinds of security. Conclusions We find support for inclusion of spatial signatures in the testing of crime drop hypotheses and for explanations of the crime drop and residential burglary in the Canadian context.

This paper exhaustively explores the threat landscape of coordinated spatiotemporal attacks in mixed reality systems. Novel devicelevel and cross-device time translation and spatial shift attacks are launched, and their impact on deep learning based sensor fusion is evaluated. A major focus of this work is to establish stealthiness in the presence of sophisticated security mechanisms with an added constraint that mixed reality systems allow minimal time durations for covert operation. The efficacy of proposed attacks is evaluated through a preliminary study on inertial and visual data streams.

We explore attractive dipolar interaction in indirect excitons (IXs). For one layer of IXs in a single pair of coupled quantum wells (CQW), the out-of-plane IX electric dipoles lead to repulsive dipolar interaction between IXs. The attractive dipolar interaction between IXs is realized in a 2-CQW heterostructure with two IX layers in two separated CQW pairs. We found both in experimental measurements and theoretical simulations that increasing density of IXs in one layer causes a monotonic energy reduction for IXs in the other layer. We also found an in-plane shift of a cloud of IXs in one layer towards a cloud of IXs in the other layer. This behaviour is qualitatively consistent with attractive dipolar interaction. The measured IX energy reduction and IX cloud shift are higher than the values given by the correlated liquid theory.

In a typical interferometric experiment, the overlap between the reference and probe beams produces an intensity pattern composed of fringes. This fringe pattern contains information regarding the interaction of the probe beam with the sample. If the probe beam passes through a polarizing system, it acquires both a dynamic and geometric phase. The former related to the optical pathlength, whereas the latter related to changes in the state of polarization. As a consequence, the fringe pattern suffers a lateral spatial shift and, mainly due to the geometric phase, change its visibility. In this work, we derive simple expressions relating the fringe visibility with the geometric phase and retardance introduced by the polarizing optical system, in terms of the input polarization state. By doing so, we extract the eigenvectors and eigenvalues of the Jones matrix that characterizes the sample. Therefore, our results enable the implementation of a fringe polarimetry technique, i.e., we can infer the polarization properties of the test sample through visibility measurements.

The Attentional Spatial Numerical Association of Response Codes (Att-SNARC) effect has shown that number perception induces shifts in spatial attention (Fischer et al., 2003; Dodd et al., 2008). However, many replications were attempted and they often failed. In the present study, we investigated whether the Att-SNARC effect can be found for numbers in different notations: months in Arabic form, Simplified Chinese form, Traditional Chinese form (includes numerical ordinal information) and in Chinese non-numerical form (an ordinal sequence). By varying the cognitive task, we also examined whether the effect is a consequence of automatic perceptual processing. In Experiment 1, an Att-SNARC effect was observed for numbers regardless of notation. In Experiment 2 (order-irrelevant task) and Experiment 3 (order-relevant task), the effect was also found consistently for months in Arabic form, Simplified Chinese form, and Traditional Chinese form. This effect was not observed for months in Chinese non-numerical form in Experiment 3. These results show that number and numerical sequence perception automatically causes a spatial shift of attention. Our study provides positive evidence for the Att-SNARC effect and indicates that the effect can generalize to other numerical ordinal sequences that contain numeric information.

The Osu shopping district in Nagoya has adapted to changes in the external environment by altering the configuration of its shops by the type of products they sell. This chapter looks at a 2013 survey centered on shop-around behaviors and the configuration of shops in the district and compares it with an earlier one conducted in 2008. By analyzing the changes across the two surveys, a dynamic model of factors behind those changes emerges, illustrated in this chapter through a series of tables and charts. Shop distribution in the Osu district was recorded via a field study, and shop tenants were invited to complete a questionnaire on such factors as visitor attributes, their walking routes, and their purposes of visit. The findings included: (1) patterns of change in shop types during this period; (2) a spatial shift in the visitors’ behaviors; and (3) crowding indicators as potential factors behind shop tenant changes. Additionally, a relationship between the amount of walking around by visitors and the density of shops was confirmed.