Journal of the Society for Information Display
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SCImago
Q2
WOS
Q3
Impact factor
1.7
SJR
0.588
CiteScore
4.8
Categories
Atomic and Molecular Physics, and Optics
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials
Areas
Engineering
Materials Science
Physics and Astronomy
Years of issue
1993-2025
journal names
Journal of the Society for Information Display
J SOC INF DISPLAY
Top-3 citing journals

SID Symposium Digest of Technical Papers
(2431 citations)

Journal of the Society for Information Display
(1865 citations)

Optics Express
(775 citations)
Top-3 organizations

Royal Philips
(104 publications)

Samsung
(95 publications)

Hong Kong University of Science and Technology
(65 publications)

University of Central Florida
(20 publications)

Samsung
(14 publications)

Hong Kong University of Science and Technology
(12 publications)
Most cited in 5 years
Found
Publications found: 2492
Q1

The Stochastic Dynamic Postdisaster Inventory Allocation Problem with Trucks and UAVs
van Steenbergen R.M., van Heeswijk W.J., Mes M.R.
Humanitarian logistics operations face increasing difficulties due to rising demands for aid in disaster areas. This paper investigates the dynamic allocation of scarce relief supplies across multiple affected districts over time. It introduces a novel stochastic dynamic postdisaster inventory allocation problem (SDPDIAP) with trucks and unmanned aerial vehicles (UAVs) delivering relief goods under uncertain supply and demand. The relevance of this humanitarian logistics problem lies in the importance of considering the intertemporal social impact of deliveries. We achieve this by considering social costs (transportation and deprivation costs) when allocating scarce supplies. Furthermore, we consider the inherent uncertainties of disaster areas and the potential use of cargo UAVs to enhance operational efficiency. This study proposes two anticipatory solution methods based on approximate dynamic programming, specifically decomposed linear value function approximation (DL-VFA) and neural network value function approximation (NN-VFA) to effectively manage uncertainties in the dynamic allocation process. We compare DL-VFA and NN-VFA with various state-of-the-art methods (e.g., exact reoptimization and proximal policy optimization) and results show a 6%–8% improvement compared with the best benchmarks. NN-VFA provides the best performance and captures nonlinearities in the problem, whereas DL-VFA shows excellent scalability against a minor performance loss. From a practical standpoint, the experiments reveal that consideration of social costs results in improved allocation of scarce supplies both across affected districts and over time. Finally, results show that deploying UAVs can play a crucial role in the allocation of relief goods, especially in the first stages after a disaster. The use of UAVs reduces transportation and deprivation costs together by 16%–20% and reduces maximum deprivation times by 19%–40% while maintaining similar levels of demand coverage, showcasing efficient and effective operations. History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023.
Q1

Solving the Real-Time Train Dispatching Problem by Column Generation
Schälicke M., Nachtigall K.
Disruptions in the operational flow of rail traffic can lead to conflicts between train movements, making it impossible to adhere to the scheduled timetable. This is when dispatching comes into play: resolving existing conflicts and providing a revised timetable. In this process, train paths are adjusted in their spatial and temporal dimensions. This adjustment is known as the train dispatching problem (TDP), which involves selecting conflict-free train paths with minimal delay. Starting from a path-oriented formulation of the TDP, a binary linear decision model is introduced. For each possible train path, a binary decision variable indicates whether the path is utilized by a train. Each train path is constructed from a set of predefined path segments within a time–space network. Instead of modeling pairwise conflicts, stronger linear programming formulations are achieved by defining cliques over the complete train paths. The combinatorial nature of the path segments results in a large number of possible paths, necessitating the use of the column-generation method. Within the subproblem, the shadow prices of conflict cliques must be considered. When constructing a new train path, it must be determined whether it belongs to a clique. This issue is addressed using a mixed integer program. The methodology is tested on instances from a dispatching area in Germany. Numerical results show that the presented method achieves acceptable computation times and good solution quality, meeting the requirements for real-time dispatching.
Q1

Estimating Erratic Measurement Errors in Network-Wide Traffic Flow via Virtual Balance Sensors
Zheng Z., Wang Z., Fu H., Ma W.
Large-scale traffic flow data are collected by numerous sensors for managing and operating transport systems. However, various measurement errors exist in the sensor data and their distributions or structures are usually not known in the real world, which diminishes the reliability of the collected data and impairs the performance of smart mobility applications. Such irregular error is referred to as the erratic measurement error and has not been well investigated in existing studies. In this research, we propose to estimate the erratic measurement errors in networked traffic flow data. Different from existing studies that mainly focus on measurement errors with known distributions or structures, we allow the distributions and structures of measurement errors to be unknown except that measurement errors occur based on a Poisson process. By exploiting the flow balance law, we first introduce the concept of virtual balance sensors and develop a mixed integer nonlinear programming model to simultaneously estimate sensor error probabilities and recover traffic flow. Under suitable assumptions, the complex integrated problem can be equivalently viewed as an estimate-then-optimize problem: first, estimation using machine learning (ML) methods, and then optimization with mathematical programming. When the assumptions fail in more realistic scenarios, we further develop a smart estimate-then-optimize (SEO) framework that embeds the optimization model into ML training loops to solve the problem. Compared with the two-stage method, the SEO framework ensures that the optimization process can recognize and compensate for inaccurate estimations caused by ML methods, which can produce more reliable results. Finally, we conduct numerical experiments using both synthetic and real-world examples under various scenarios. Results demonstrate the effectiveness of our decomposition approach and the superiority of the SEO framework. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods for Urban Mobility. Funding: The work described in this paper was supported by the National Natural Science Foundation of China [Grant Project No. 72288101, 72101012, 72301023] and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China [Grant Project No. PolyU/15206322]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0493 .
Q1

Exact Two-Step Benders Decomposition for the Time Window Assignment Traveling Salesperson Problem
Çelik Ş., Martin L., Schrotenboer A.H., Van Woensel T.
Next-day delivery logistics services are redefining the industry by increasingly focusing on customer service. Each logistics service provider’s challenge is jointly optimizing time window assignment and vehicle routing for such next-day delivery services. To do so in a cost-efficient and customer-centric fashion, real-life uncertainty, such as stochastic travel times, needs to be incorporated into the optimization process. This paper focuses on the canonical optimization problem within this context: the time window assignment traveling salesperson problem with stochastic travel times (TWATSP-ST). It belongs to two-stage, stochastic, mixed-integer programming problems with continuous recourse. We introduce two-step Benders decomposition with scenario clustering (TBDS) as an exact solution methodology for solving such stochastic programs. The method utilizes a new two-step decomposition along the binary and continuous first stage decisions and introduces a new scenario-retention strategy that combines and generalizes state-of-the-art Benders approaches and scenario-clustering techniques. Extensive experiments show that TBDS is superior to state-of-the-art approaches in the literature. It solves TWATSP-ST instances with up to 25 customers to optimality. It provides better lower and upper bounds that lead to faster convergence than existing state-of-the-art methods. We use TBDS to analyze the structure of the optimal solutions. By increasing routing costs only slightly, customer service can be improved tremendously driven by smartly alternating between high- and low-variance travel arcs to reduce the impact of delay propagation throughout the executed vehicle route. Funding: A. H. Schrotenboer has received support from the Dutch Science Foundation [Grant VI.Veni.211E.043]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0750 .
Q1

Modeling Metro Passenger Routing Choices with a Fully Differentiable End-to-End Simulation-Based Optimization (SBO) Approach
Du K., Lee E., Ma Q., Su Z., Zhang S., Lo H.K.
Metro systems in densely populated urban areas are often complicated, with some origin-destinations (OD) having multiple routes with similar travel times, leading to complex passenger routing behaviors. To improve modeling and calibration, this paper proposes a novel passenger route choice model with a metro simulator that accounts for passenger flows, queueing, congestion, and transfer delays. A novel, data-driven approach that utilizes a fully differentiable end-to-end simulation-based optimization (SBO) framework is proposed to calibrate the model, with the gradients calculated automatically and analytically using the iterative backpropagation (IB) algorithm. The SBO framework integrates data from multiple sources, including smart card data and train loadings, to calibrate the route choice parameters that best match the observed data. The full differentiability of the proposed framework enables it to calibrate for more than 20,000 passenger route choice ratios, covering every OD pair. To further improve the efficiency of the framework, a matrix-based optimization (MBO) mechanism is proposed, which provides better initial values for the SBO and ensures high efficiency with large datasets. A hybrid optimization algorithm combining MBO and SBO effectively calibrates the model, demonstrating high accuracy with synthetic data from actual passenger OD demands, where hypothesis tests are conducted for accuracies and significances. The accuracies and robustness are validated by experiments with synthetic passenger flow data, offering potential for optimizing passenger flow management in densely populated urban metro systems. Then, the SBO framework is extended for user equilibrium formulations with a crowding-aware route choice model and iterative metro simulations, calibrated by the hybrid optimization algorithm with additional matrix operations. Case studies with actual observed passenger flows are conducted to illustrate the proposed framework with multiple setups, exhibiting the heterogeneity of passenger route choice preferences and providing insights for operation management in the Hong Kong Mass Transit Railway system. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods for Urban Mobility. Funding: This work was supported by the General Research Fund of the Research Grants Council of Hong Kong [Grant 16219224], the Key Research and Development Program of Hubei Province [Grant 2023BAB076], and the National Natural Science Foundation of China [Grant 72001162]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0557 .
Q1

2024 Transportation Science Meritorious Service Awards
We are pleased to announce the recipients of the Transportation Science Meritorious Service Awards. These awards recognize associate editors, special issue guest editors, and reviewers who have offered exceptional service in the review process. We truly appreciate all the efforts of the many volunteers who provide invaluable service to the journal. The 2024 recipients have distinguished themselves by the number of papers handled, their efficiency in handling papers, and the quality of their reviews.
Q1

Dynamic Robot Routing and Destination Assignment Policies for Robotic Sorting Systems
Fang Y., De Koster R., Roy D., Yu Y.
Robotic sorting systems (RSSs) use mobile robots to sort items by destination. An RSS pairs high accuracy and flexible capacity sorting with the advantages of a flexible layout. This is why several express parcel and e-commerce retail companies, who face heavy demand fluctuations, have implemented these systems. To cope with fluctuating demand, temporal robot congestion, and high sorting speed requirements, workload balancing strategies such as dynamic robot routing and destination reassignment may be of benefit. We investigate the effect of a dynamic robot routing policy using a Markov decision process (MDP) model and dynamic destination assignment using a mixed integer programming (MIP) model. To obtain the MDP model parameters, we first model the system as a semiopen queuing network (SOQN) that accounts for robot movement dynamics and network congestion. Then, we construct the MIP model to find a destination reassignment scheme that minimizes the workload imbalance. With inputs from the SOQN and MIP models, the Markov decision process minimizes parcel waiting and postponement costs and helps to find a good heuristic robot routing policy to reduce congestion. We show that the heuristic dynamic routing policy is near optimal in small-scale systems and outperforms benchmark policies in large-scale realistic scenarios. Dynamic destination reassignment also has positive effects on the throughput capacity in highly loaded systems. Together, in our case company, they improve the throughput capacity by 35%. Simultaneously, the effect of dynamic routing exceeds that of dynamic destination reassignment, suggesting that managers should focus more on dynamic robot routing than dynamic destination reassignment to mitigate temporal congestion. Funding: This work was supported by The Fundamental Research Funds for the Central Universities [Grant WK2040000094] and the National Natural Science Foundation of China [Grants 71921001 and 72091215/72091210]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0458 .
Q1

Multimodal Transportation Pricing Alliance Design: Large-Scale Optimization for Rapid Gains
Cummings K., Vaze V., Ergun Ö., Barnhart C.
Transit agencies have the opportunity to outsource certain services to established mobility-on-demand (MOD) providers. Such alliances can improve service quality, coverage, and ridership; reduce public sector costs and vehicular emissions; and integrate the passenger experience. To amplify the effectiveness of such alliances, we develop a fare-setting model that jointly optimizes fares and discounts across a multimodal network. We capture commuters’ travel decisions with a discrete choice model, resulting in a large-scale, mixed-integer, nonconvex optimization problem. To solve this challenging problem, we develop a two-stage decomposition with the pricing decisions in the first stage and a mixed-integer linear optimization of fare discounts and passengers’ travel decisions in the second stage. To solve the decomposition, we develop a new solution approach that combines customized coordinate descent, parsimonious second-stage evaluations, and interpolations using special ordered sets. This approach, enhanced by acceleration techniques based on slanted traversal, randomization, and warm-start, significantly outperforms algorithmic benchmarks. Different alliance priorities result in qualitatively different fare designs: flat fares decrease the total vehicle-miles traveled, whereas geographically informed discounts improve passenger happiness. The model responds appropriately to equity-oriented and passenger-centric priorities, improving system utilization and lowering prices for low-income and long-distance commuters. Our profit allocation mechanism improves the outcomes for both types of operators, thus incentivizing profit-oriented MOD operators to adopt transit priorities. Funding: This material is based on work supported by the National Science Foundation [Grants 1122374 and 1750587]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0009 .
Q1

Designing the Liner Shipping Network of Tomorrow Powered by Alternative Fuels
Johansen M.L., Holst K.K., Ropke S.
The liner shipping industry is undergoing an extensive decarbonization process to reduce its 275 million tons of CO2 emissions as of 2018. In this process, the long-term solution is the introduction of new alternative maritime fuels. The introduction of alternative fuels presents a great set of unknowns. Among these are the strategic concerns regarding sourcing of alternative fuels and, operationally, how the new fuels might affect the network of shipping routes. We propose a problem formulation that integrates fuel supply/demand into the liner shipping network design problem. Here, we present a model to determine the production sites and distribution of new alternative fuels—we consider methanol and ammonia. For the network design problem, we apply an adaptive large neighborhood search combined with a delayed column generation process. In addition, we wish to test the effect of designing a robust network under uncertain demand conditions because of the problem’s strategic nature and importance. Therefore, our proposed solution method will have a deterministic and stochastic setup when we apply it to the second-largest multihub instance, WorldSmall, known from LINER-LIB. In the deterministic setting, our proposed solution method finds a new best solution to three instances from LINER-LIB. For the main considered WorldSmall instance, we even noticed a new best solution in all our tested fuel settings. In addition, we note a profit drop of 7.2% between a bunker-powered and pure alternative fuel–powered network. The selected alternative fuel production sites favor a proximity to European ports and have a heavy reliance on wind turbines. The stochastic results clearly showed that the found networks were much more resilient to the demand changes. Neglecting the perspective of uncertain demand leads to highly fluctuating profits. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0143 .
Q1

A Two-Stage Iteration Method for Solving the Departure Time Choice Problem
Guo R., Yang H., Huang H.
We discuss the numerical solution of the departure time choice problem. The non–quasi-monotone of the travel cost vector function is first proved to address the study motivation. A two-stage iteration method is then proposed to effectively solve the problem in a single origin-destination (OD) pair network with parallel links, in which departure time and route choices of commuters are involved. We analytically reveal why the iteration method can solve the problem and theoretically prove the convergence, that is, the iteration process finally achieves at a user equilibrium (UE) state. The iteration method is then extended to a single link network with heterogeneous users in the values of travel time and schedule delay and the preferred arrival time. Furthermore, numerical analyses are conducted for the two networks to demonstrate the effectiveness of the iteration method for solving the departure time choice problem. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72288101, 72171007, and 72021001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0599 .
Q1

The Restaurant Meal Delivery Problem with Ghost Kitchens
Neria G., Hildebrandt F.D., Tzur M., Ulmer M.W.
Restaurant meal delivery has been rapidly growing in the last few years. The main operational challenges are the temporally and spatially dispersed stochastic demand that arrives from customers all over town as well as the customers’ expectation of timely and fresh delivery. To overcome these challenges, a new business concept emerged: ghost kitchens. This concept proposes synchronized food preparation of several restaurants in a central facility. Ghost kitchens can bring several advantages, such as fresher food because of the synchronization of food preparation and delivery and less delay because of the consolidated delivery of orders. Exploiting these advantages requires effective operational strategies for the dynamic scheduling of food preparation and delivery. The goal of this paper is providing these strategies and investigating the value of ghost kitchens. We model the problem as a sequential decision process. For the complex decision space of scheduling order preparations, consolidating orders to trips, and scheduling trip departures, we propose a large neighborhood search (LNS) procedure based on partial decisions and driven by analytical properties. Within the LNS, decisions are evaluated via a value function approximation, enabling anticipatory and real-time decision making. In a comprehensive computational study, we demonstrate the effectiveness of our method compared with benchmark policies and highlight the advantages of ghost kitchens compared with conventional meal delivery. Funding: G. Neria’s research is partially supported by the Israeli Smart Transportation Research Center, the Council for Higher Education in Israel, and the Shlomo Shmeltzer Institute for Smart Transportation at Tel Aviv University. F. D. Hildebrandt’s research is funded by the Deutsche Forschungsgemeinschaft (DFG) German Research Foundation [Grant 413322447]. M. Tzur’s research is partially supported by the Israeli Smart Transportation Research Center. M. W. Ulmer’s work is funded by the DFG Emmy Noether Programme [Grant 444657906]. We gratefully acknowledge their support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0510 .
Q1

Multiday User Equilibrium with Strategic Commuters
Wu M., Yin Y., Lynch J.P.
In the era of connected and automated mobility, commuters possess strong computation power, enabling them to strategically make sequential travel choices over a planning horizon. This paper investigates the multiday traffic patterns that arise from such decision-making behavior. In doing so, we frame the commute problem as a mean-field Markov game and introduce a novel concept of multiday user equilibrium to capture the steady state of commuters’ interactions. The proposed model is general and can be tailored to various travel choices, such as route or departure time. We explore a range of properties of the multiday user equilibrium under mild conditions. The study reveals the fingerprint of user inertia on network flow patterns, causing between-day variations even at a steady state. Furthermore, our analysis establishes critical connections between the multiday user equilibrium and conventional Wardrop equilibrium. Funding: This work was supported by the National Science Foundation [Grants CMMI-1854684, CMMI-1904575, CMMI-2233057, and CMMI-2240981]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0488 .
Q1

On the Concept of Opportunity Cost in Integrated Demand Management and Vehicle Routing
Fleckenstein D., Klein R., Klein V., Steinhardt C.
Integrated demand management and vehicle routing problems are characterized by a stream of customers arriving dynamically over a booking horizon and requesting logistical services, fulfilled by a given fleet of vehicles during a service horizon. Prominent examples are attended home delivery and same-day delivery problems, where customers commonly have heterogeneous preferences regarding service fulfillment and requests differ in profitability. Thus, demand management methods are applied to steer the booking process to maximize total profit considering the cost of the routing decisions for the resulting orders. To measure the requests’ profitability for any demand management method, it is common to estimate their opportunity cost. In the context of integrated demand management and vehicle routing problems, this estimation differs substantially from the estimation in the well-examined demand management problems of traditional revenue management applications as, for example, found in the airline or car rental industry. This is because of the unique interrelation of demand control decisions and vehicle routing decisions as it inhibits a clear quantification and attribution of cost, and of displaced revenue, to certain customer requests. In this paper, we extend the theoretical foundation of opportunity cost in integrated demand management and vehicle routing problems. By defining and analyzing a generic Markov decision process model, we formally derive a definition of opportunity cost and prove opportunity cost properties on a general level. Hence, our findings are valid for a wide range of specific problems. Further, based on these theoretical findings, we propose approximation approaches that have not yet been applied in the existing literature, and evaluate their potential in a computational study. Thereby, we provide evidence that the theoretical results can be practically exploited in the development of solution algorithms. Funding: This work was supported by the University of the Bundeswehr Munich. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2024.0644 .
Q1

A 0,1 Linear Programming Approach to Deadlock Detection and Management in Railways
Dal Sasso V., Lamorgese L., Mannino C., Onofri A., Ventura P.
In railway systems, a deadlock occurs when trains accidentally occupy positions that prevent each other from moving forward. Although deadlocks are rare events, they do occur from time to time, requiring costly recourse actions and generating significant knock-on delays. In this paper, we present a noncompact 0,1 linear programming formulation and a methodology for discovering (possibly future) deadlocks and the subsequent implementation of optimal recovery measures. The approach is implemented in a tool to dispatch trains in real time developed in cooperation with Union Pacific (UP) and currently in operations on the entire UP network. Funding: This work was partially funded by Europe’s Rail, Flagship Project MOTIONAL [Action Horizon JU Innovation, Project 101101973]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0521 .
Q1

Pricing and Demand Management for Integrated Same-Day and Next-Day Delivery Systems
Banerjee D., Erera A.L., Toriello A.
We study a system in which a common delivery fleet provides service to both same-day delivery (SDD) and next-day delivery (NDD) orders placed by e-retail customers who are sensitive to delivery prices. We develop a model of the system and optimize with respect to two separate objectives. First, empirical research suggests that fulfilling e-retail orders ahead of promised delivery days increases a firm’s long-run market share. Motivated by this phenomenon, we optimize for customer satisfaction by maximizing the quantity of NDD orders fulfilled one day early given fixed prices. Next, we optimize for total profit; we optimize for a single SDD price, and we then set SDD prices in a two-level scheme with discounts for early-ordering customers. Our analysis relies on continuous approximation techniques to capture the interplay between NDD and SDD orders and particularly the effect one day’s operations have on the next, a novel modeling component not present in SDD-only models; a key technical result is establishing the model’s convergence to a steady state using dynamical systems theory. We derive structural insights and efficient algorithms for both objectives. In particular, we show that, under certain conditions, the total profit is a piecewise-convex function with polynomially many breakpoints that can be efficiently enumerated. In a case study set in metropolitan Denver, Colorado, approximately 10% of NDD orders can be fulfilled one day early at optimality, and profit is increased by 1% to 3% in a two-level pricing scheme versus a one-level scheme. We conduct operational simulations for validation of solutions and analysis of initial conditions. History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023. Funding: This work was supported by the National Science Foundation [Grant DGE-1650044]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0381 .
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Journal of Micromechanics and Microengineering
31 citations, 0.13%
|
|
Show all (70 more) | |
500
1000
1500
2000
2500
|
Citing publishers
1000
2000
3000
4000
5000
6000
7000
|
|
Wiley
6227 citations, 25.78%
|
|
Institute of Electrical and Electronics Engineers (IEEE)
3094 citations, 12.81%
|
|
Elsevier
2720 citations, 11.26%
|
|
Springer Nature
1558 citations, 6.45%
|
|
Optica Publishing Group
1431 citations, 5.92%
|
|
MDPI
1030 citations, 4.26%
|
|
Taylor & Francis
925 citations, 3.83%
|
|
American Chemical Society (ACS)
912 citations, 3.78%
|
|
AIP Publishing
784 citations, 3.25%
|
|
Royal Society of Chemistry (RSC)
700 citations, 2.9%
|
|
IOP Publishing
624 citations, 2.58%
|
|
Japan Society of Applied Physics
431 citations, 1.78%
|
|
SPIE-Intl Soc Optical Eng
314 citations, 1.3%
|
|
The Electrochemical Society
164 citations, 0.68%
|
|
Association for Computing Machinery (ACM)
161 citations, 0.67%
|
|
Frontiers Media S.A.
113 citations, 0.47%
|
|
American Physical Society (APS)
96 citations, 0.4%
|
|
SAGE
94 citations, 0.39%
|
|
American Vacuum Society
85 citations, 0.35%
|
|
Hindawi Limited
65 citations, 0.27%
|
|
Pleiades Publishing
58 citations, 0.24%
|
|
Trans Tech Publications
54 citations, 0.22%
|
|
Cambridge University Press
44 citations, 0.18%
|
|
Institution of Engineering and Technology (IET)
43 citations, 0.18%
|
|
American Association for the Advancement of Science (AAAS)
37 citations, 0.15%
|
|
Institute of Electronics, Information and Communications Engineers (IEICE)
35 citations, 0.14%
|
|
Walter de Gruyter
30 citations, 0.12%
|
|
30 citations, 0.12%
|
|
29 citations, 0.12%
|
|
Korean Journal of Optics and Photonics
26 citations, 0.11%
|
|
Acta Physica Sinica, Chinese Physical Society and Institute of Physics, Chinese Academy of Sciences
25 citations, 0.1%
|
|
Public Library of Science (PLoS)
24 citations, 0.1%
|
|
Oxford University Press
19 citations, 0.08%
|
|
Institute of Image Information and Television Engineers
19 citations, 0.08%
|
|
Eizo Joho Media Gakkai
19 citations, 0.08%
|
|
Institute of Electrical Engineers of Japan (IEE Japan)
19 citations, 0.08%
|
|
Association for Research in Vision and Ophthalmology (ARVO)
18 citations, 0.07%
|
|
National Academy of Sciences of Ukraine - Institute of Semiconductor Physics
18 citations, 0.07%
|
|
Ovid Technologies (Wolters Kluwer Health)
17 citations, 0.07%
|
|
Annual Reviews
17 citations, 0.07%
|
|
World Scientific
16 citations, 0.07%
|
|
The Technical Association of Photopolymers, Japan
16 citations, 0.07%
|
|
Korean Institute of Metals and Materials
15 citations, 0.06%
|
|
The Laser Society of Japan
15 citations, 0.06%
|
|
IOS Press
14 citations, 0.06%
|
|
The Royal Society
13 citations, 0.05%
|
|
OAE Publishing Inc.
13 citations, 0.05%
|
|
IntechOpen
13 citations, 0.05%
|
|
Mary Ann Liebert
12 citations, 0.05%
|
|
Society of Motion and Television Engineers
12 citations, 0.05%
|
|
Shanghai Institute of Optics and Fine Mechanics
12 citations, 0.05%
|
|
Proceedings of the National Academy of Sciences (PNAS)
11 citations, 0.05%
|
|
Polymer Society of Korea
11 citations, 0.05%
|
|
ASME International
10 citations, 0.04%
|
|
Scientific Research Publishing
10 citations, 0.04%
|
|
Society for Imaging Science & Technology
10 citations, 0.04%
|
|
American Scientific Publishers
9 citations, 0.04%
|
|
Korean Society of Industrial Engineering Chemistry
9 citations, 0.04%
|
|
The Korean Society of Precision Engineering
9 citations, 0.04%
|
|
Science in China Press
9 citations, 0.04%
|
|
IGI Global
9 citations, 0.04%
|
|
8 citations, 0.03%
|
|
The Korean Institute of Electrical and Electronic Material Engineers
8 citations, 0.03%
|
|
The Chemical Society of Japan
8 citations, 0.03%
|
|
EDP Sciences
7 citations, 0.03%
|
|
Ceramic Society of Japan
7 citations, 0.03%
|
|
Cold Spring Harbor Laboratory
7 citations, 0.03%
|
|
Beilstein-Institut
6 citations, 0.02%
|
|
Opto-Electronic Advances
6 citations, 0.02%
|
|
Emerald
5 citations, 0.02%
|
|
Georg Thieme Verlag KG
5 citations, 0.02%
|
|
Korean Society of Mechanical Engineers
5 citations, 0.02%
|
|
MIT Press
5 citations, 0.02%
|
|
Tsinghua University Press
5 citations, 0.02%
|
|
The Korean Fiber Society
5 citations, 0.02%
|
|
SAE International
5 citations, 0.02%
|
|
Research Square Platform LLC
5 citations, 0.02%
|
|
Higher Education Press
4 citations, 0.02%
|
|
Japan Academy
4 citations, 0.02%
|
|
The Electrochemical Society of Japan
4 citations, 0.02%
|
|
JMIR Publications
4 citations, 0.02%
|
|
The Surface Science Society of Japan
4 citations, 0.02%
|
|
Bentham Science Publishers Ltd.
3 citations, 0.01%
|
|
Society for Industrial and Applied Mathematics (SIAM)
3 citations, 0.01%
|
|
Image Processing Systems Institute of RAS
3 citations, 0.01%
|
|
Japan Institute of Metals
3 citations, 0.01%
|
|
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences
3 citations, 0.01%
|
|
Chinese Society of Rare Earths
3 citations, 0.01%
|
|
Japanese Society for Hygiene
3 citations, 0.01%
|
|
Uspekhi Fizicheskikh Nauk Journal
3 citations, 0.01%
|
|
China Science Publishing & Media
3 citations, 0.01%
|
|
Social Science Electronic Publishing
3 citations, 0.01%
|
|
Physical Society of Japan
3 citations, 0.01%
|
|
Japanese Society of Radiological Technology
3 citations, 0.01%
|
|
The Japan Society for Precision Engineering
3 citations, 0.01%
|
|
Japan Society of Mechanical Engineers
2 citations, 0.01%
|
|
2 citations, 0.01%
|
|
Acoustical Society of America (ASA)
2 citations, 0.01%
|
|
AME Publishing Company
2 citations, 0.01%
|
|
Nonferrous Metals Society of China
2 citations, 0.01%
|
|
Show all (70 more) | |
1000
2000
3000
4000
5000
6000
7000
|
Publishing organizations
20
40
60
80
100
120
|
|
Royal Philips
104 publications, 3.51%
|
|
Samsung
95 publications, 3.2%
|
|
Hong Kong University of Science and Technology
65 publications, 2.19%
|
|
National Yang Ming Chiao Tung University
53 publications, 1.79%
|
|
Southeast University
50 publications, 1.69%
|
|
Sichuan University
49 publications, 1.65%
|
|
University of Central Florida
49 publications, 1.65%
|
|
Toshiba Corporation
46 publications, 1.55%
|
|
Seoul National University
41 publications, 1.38%
|
|
Sony Group Corporation
41 publications, 1.38%
|
|
Kyung Hee University
34 publications, 1.15%
|
|
Tohoku University
33 publications, 1.11%
|
|
Ghent University
32 publications, 1.08%
|
|
Mitsubishi Electric Corporation
28 publications, 0.94%
|
|
Interuniversity Microelectronics Centre
26 publications, 0.88%
|
|
Fujitsu Limited
26 publications, 0.88%
|
|
Hanyang University
22 publications, 0.74%
|
|
University of Electro-Communications
21 publications, 0.71%
|
|
Beijing Institute of Technology
20 publications, 0.67%
|
|
National Taiwan University
19 publications, 0.64%
|
|
Seoul National University of Science and Technology
19 publications, 0.64%
|
|
National Taiwan University of Science and Technology
17 publications, 0.57%
|
|
National Chung Hsing University
17 publications, 0.57%
|
|
Panasonic Holdings Corporation
17 publications, 0.57%
|
|
Beihang University
15 publications, 0.51%
|
|
Sun Yat-sen University
15 publications, 0.51%
|
|
University of Cambridge
15 publications, 0.51%
|
|
National Tsing Hua University
15 publications, 0.51%
|
|
Georgia Institute of technology
15 publications, 0.51%
|
|
Korea Advanced Institute of Science and Technology
14 publications, 0.47%
|
|
University of Stuttgart
14 publications, 0.47%
|
|
Kyushu University
14 publications, 0.47%
|
|
Brown University
13 publications, 0.44%
|
|
Shizuoka University
13 publications, 0.44%
|
|
South China University of Technology
12 publications, 0.4%
|
|
Southern University of Science and Technology
12 publications, 0.4%
|
|
Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute
12 publications, 0.4%
|
|
Industrial Technology Research Institute
12 publications, 0.4%
|
|
Chiba University
12 publications, 0.4%
|
|
University of Colorado Boulder
12 publications, 0.4%
|
|
P.N. Lebedev Physical Institute of the Russian Academy of Sciences
11 publications, 0.37%
|
|
Belarusian State University of Informatics and Radioelectronics
11 publications, 0.37%
|
|
Katholieke Universiteit Leuven
11 publications, 0.37%
|
|
Belarusian State University
10 publications, 0.34%
|
|
Tokyo University of Agriculture and Technology
10 publications, 0.34%
|
|
Nippon Electric Company
10 publications, 0.34%
|
|
Zhejiang University
9 publications, 0.3%
|
|
Shanghai Jiao Tong University
9 publications, 0.3%
|
|
Eindhoven University of Technology
9 publications, 0.3%
|
|
Tokyo University of Science
9 publications, 0.3%
|
|
Pennsylvania State University
9 publications, 0.3%
|
|
Yonsei University
9 publications, 0.3%
|
|
Sungkyunkwan University
9 publications, 0.3%
|
|
Hongik University
9 publications, 0.3%
|
|
Food and Drug Administration
9 publications, 0.3%
|
|
Institute of Chemistry of New Materials of the National Academy of Sciences of Belarus
8 publications, 0.27%
|
|
Peking University
8 publications, 0.27%
|
|
Fuzhou University
8 publications, 0.27%
|
|
Nanyang Technological University
8 publications, 0.27%
|
|
University of Edinburgh
8 publications, 0.27%
|
|
Stanford University
8 publications, 0.27%
|
|
Korea University
8 publications, 0.27%
|
|
Arizona State University
8 publications, 0.27%
|
|
National Institute of Standards and Technology
8 publications, 0.27%
|
|
Dankook University
8 publications, 0.27%
|
|
Harvard University
8 publications, 0.27%
|
|
University of Arizona
8 publications, 0.27%
|
|
Hoseo University
8 publications, 0.27%
|
|
Nagoya University
8 publications, 0.27%
|
|
University of Michigan
8 publications, 0.27%
|
|
Tottori University
8 publications, 0.27%
|
|
Beijing Film Academy
8 publications, 0.27%
|
|
Tsinghua University
7 publications, 0.24%
|
|
South China Normal University
7 publications, 0.24%
|
|
Chengdu University of Technology
7 publications, 0.24%
|
|
Chung Yuan Christian University
7 publications, 0.24%
|
|
Princeton University
7 publications, 0.24%
|
|
Korea Institute of Science and Technology
7 publications, 0.24%
|
|
Kyungpook National University
7 publications, 0.24%
|
|
Chonbuk National University
7 publications, 0.24%
|
|
University of California, San Diego
7 publications, 0.24%
|
|
Osaka University
7 publications, 0.24%
|
|
University of Florida
7 publications, 0.24%
|
|
State University of Education
6 publications, 0.2%
|
|
Delft University of Technology
6 publications, 0.2%
|
|
National Central University
6 publications, 0.2%
|
|
Feng Chia University
6 publications, 0.2%
|
|
Columbia University
6 publications, 0.2%
|
|
Washington State University
6 publications, 0.2%
|
|
Pohang University of Science and Technology
6 publications, 0.2%
|
|
Oregon State University
6 publications, 0.2%
|
|
Korea Aerospace University
6 publications, 0.2%
|
|
Utsunomiya University
6 publications, 0.2%
|
|
Ryukoku University
6 publications, 0.2%
|
|
De Montfort University
6 publications, 0.2%
|
|
Lomonosov Moscow State University
5 publications, 0.17%
|
|
Raman Research Institute
5 publications, 0.17%
|
|
Dalian Maritime University
5 publications, 0.17%
|
|
National Cheng Kung University
5 publications, 0.17%
|
|
Tokyo Institute of Technology
5 publications, 0.17%
|
|
Show all (70 more) | |
20
40
60
80
100
120
|
Publishing organizations in 5 years
5
10
15
20
|
|
University of Central Florida
20 publications, 5.21%
|
|
Samsung
14 publications, 3.65%
|
|
Hong Kong University of Science and Technology
12 publications, 3.13%
|
|
Sun Yat-sen University
9 publications, 2.34%
|
|
Kyung Hee University
9 publications, 2.34%
|
|
Southeast University
8 publications, 2.08%
|
|
Beijing Institute of Technology
7 publications, 1.82%
|
|
Peking University
7 publications, 1.82%
|
|
National Yang Ming Chiao Tung University
7 publications, 1.82%
|
|
Sichuan University
6 publications, 1.56%
|
|
Fuzhou University
6 publications, 1.56%
|
|
Southern University of Science and Technology
6 publications, 1.56%
|
|
Sungkyunkwan University
6 publications, 1.56%
|
|
Beihang University
5 publications, 1.3%
|
|
Chengdu University of Technology
5 publications, 1.3%
|
|
Seoul National University of Science and Technology
5 publications, 1.3%
|
|
Tsinghua University
4 publications, 1.04%
|
|
South China University of Technology
4 publications, 1.04%
|
|
Interuniversity Microelectronics Centre
4 publications, 1.04%
|
|
University of Colorado Boulder
4 publications, 1.04%
|
|
Zhejiang University
3 publications, 0.78%
|
|
Stanford University
3 publications, 0.78%
|
|
Columbia University
3 publications, 0.78%
|
|
Seoul National University
3 publications, 0.78%
|
|
Hanyang University
3 publications, 0.78%
|
|
Konkuk University
3 publications, 0.78%
|
|
Kunming University of Science and Technology
3 publications, 0.78%
|
|
Yunnan Normal University
3 publications, 0.78%
|
|
Korea Aerospace University
3 publications, 0.78%
|
|
Food and Drug Administration
3 publications, 0.78%
|
|
Panasonic Holdings Corporation
3 publications, 0.78%
|
|
Chiba University
3 publications, 0.78%
|
|
Beijing Film Academy
3 publications, 0.78%
|
|
Institute of Chemistry of New Materials of the National Academy of Sciences of Belarus
2 publications, 0.52%
|
|
Belarusian State University
2 publications, 0.52%
|
|
Delhi Technological University
2 publications, 0.52%
|
|
Huazhong University of Science and Technology
2 publications, 0.52%
|
|
Harbin Institute of Technology
2 publications, 0.52%
|
|
University of Electronic Science and Technology of China
2 publications, 0.52%
|
|
Katholieke Universiteit Leuven
2 publications, 0.52%
|
|
Nanjing University of Science and Technology
2 publications, 0.52%
|
|
Nanjing Forestry University
2 publications, 0.52%
|
|
Beijing University of Posts and Telecommunications
2 publications, 0.52%
|
|
Eindhoven University of Technology
2 publications, 0.52%
|
|
Dalian Maritime University
2 publications, 0.52%
|
|
University of Cambridge
2 publications, 0.52%
|
|
Hohai University
2 publications, 0.52%
|
|
University of Edinburgh
2 publications, 0.52%
|
|
Chengdu University
2 publications, 0.52%
|
|
National Taiwan University of Science and Technology
2 publications, 0.52%
|
|
National Taiwan University
2 publications, 0.52%
|
|
Hefei University of Technology
2 publications, 0.52%
|
|
National Taipei University of Technology
2 publications, 0.52%
|
|
Air Force Engineering University
2 publications, 0.52%
|
|
Tokyo Institute of Technology
2 publications, 0.52%
|
|
Georgia Institute of technology
2 publications, 0.52%
|
|
Pennsylvania State University
2 publications, 0.52%
|
|
University of South Australia
2 publications, 0.52%
|
|
Korea Advanced Institute of Science and Technology
2 publications, 0.52%
|
|
Gyeongsang National University
2 publications, 0.52%
|
|
Catholic Kwandong University
2 publications, 0.52%
|
|
Zhengzhou University of Light Industry
2 publications, 0.52%
|
|
Tohoku University
2 publications, 0.52%
|
|
University of British Columbia
2 publications, 0.52%
|
|
Industrial Technology Research Institute
2 publications, 0.52%
|
|
German Aerospace Center
2 publications, 0.52%
|
|
University of Stuttgart
2 publications, 0.52%
|
|
University of Maryland, College Park
2 publications, 0.52%
|
|
Shinshu University
2 publications, 0.52%
|
|
Utsunomiya University
2 publications, 0.52%
|
|
University of Pennsylvania
2 publications, 0.52%
|
|
York University
2 publications, 0.52%
|
|
Volkswagen Group
1 publication, 0.26%
|
|
Ankara University
1 publication, 0.26%
|
|
University of Delhi
1 publication, 0.26%
|
|
Siksha 'O' Anusandhan
1 publication, 0.26%
|
|
Graphic Era University
1 publication, 0.26%
|
|
University of Chinese Academy of Sciences
1 publication, 0.26%
|
|
Shanghai Jiao Tong University
1 publication, 0.26%
|
|
Jilin University
1 publication, 0.26%
|
|
Xi'an Jiaotong University
1 publication, 0.26%
|
|
Karlsruhe Institute of Technology
1 publication, 0.26%
|
|
China University of Mining and Technology
1 publication, 0.26%
|
|
Ghent University
1 publication, 0.26%
|
|
University of Malaya
1 publication, 0.26%
|
|
University Putra Malaysia
1 publication, 0.26%
|
|
École Polytechnique Fédérale de Lausanne
1 publication, 0.26%
|
|
University of Lucknow
1 publication, 0.26%
|
|
Grenoble Alpes University
1 publication, 0.26%
|
|
University Tunku Abdul Rahman
1 publication, 0.26%
|
|
Nanjing Tech University
1 publication, 0.26%
|
|
ETH Zurich
1 publication, 0.26%
|
|
ZHAW Zurich University of Applied Sciences
1 publication, 0.26%
|
|
Hebei University of Science and Technology
1 publication, 0.26%
|
|
South China Normal University
1 publication, 0.26%
|
|
University of Technology Sydney
1 publication, 0.26%
|
|
Jinan University
1 publication, 0.26%
|
|
Shenzhen University
1 publication, 0.26%
|
|
University of Milan
1 publication, 0.26%
|
|
Brunel University London
1 publication, 0.26%
|
|
Show all (70 more) | |
5
10
15
20
|
Publishing countries
100
200
300
400
500
600
700
800
|
|
Japan
|
Japan, 727, 24.52%
Japan
727 publications, 24.52%
|
China
|
China, 594, 20.03%
China
594 publications, 20.03%
|
USA
|
USA, 558, 18.82%
USA
558 publications, 18.82%
|
Republic of Korea
|
Republic of Korea, 367, 12.38%
Republic of Korea
367 publications, 12.38%
|
Netherlands
|
Netherlands, 138, 4.65%
Netherlands
138 publications, 4.65%
|
Germany
|
Germany, 116, 3.91%
Germany
116 publications, 3.91%
|
United Kingdom
|
United Kingdom, 115, 3.88%
United Kingdom
115 publications, 3.88%
|
Russia
|
Russia, 50, 1.69%
Russia
50 publications, 1.69%
|
Belgium
|
Belgium, 50, 1.69%
Belgium
50 publications, 1.69%
|
France
|
France, 42, 1.42%
France
42 publications, 1.42%
|
Belarus
|
Belarus, 32, 1.08%
Belarus
32 publications, 1.08%
|
Finland
|
Finland, 31, 1.05%
Finland
31 publications, 1.05%
|
Canada
|
Canada, 25, 0.84%
Canada
25 publications, 0.84%
|
Ukraine
|
Ukraine, 24, 0.81%
Ukraine
24 publications, 0.81%
|
Switzerland
|
Switzerland, 17, 0.57%
Switzerland
17 publications, 0.57%
|
Sweden
|
Sweden, 14, 0.47%
Sweden
14 publications, 0.47%
|
India
|
India, 12, 0.4%
India
12 publications, 0.4%
|
Italy
|
Italy, 12, 0.4%
Italy
12 publications, 0.4%
|
Singapore
|
Singapore, 11, 0.37%
Singapore
11 publications, 0.37%
|
Georgia
|
Georgia, 9, 0.3%
Georgia
9 publications, 0.3%
|
Australia
|
Australia, 7, 0.24%
Australia
7 publications, 0.24%
|
Spain
|
Spain, 7, 0.24%
Spain
7 publications, 0.24%
|
Norway
|
Norway, 7, 0.24%
Norway
7 publications, 0.24%
|
Malaysia
|
Malaysia, 6, 0.2%
Malaysia
6 publications, 0.2%
|
Brazil
|
Brazil, 5, 0.17%
Brazil
5 publications, 0.17%
|
Israel
|
Israel, 4, 0.13%
Israel
4 publications, 0.13%
|
Mexico
|
Mexico, 4, 0.13%
Mexico
4 publications, 0.13%
|
Poland
|
Poland, 4, 0.13%
Poland
4 publications, 0.13%
|
Slovenia
|
Slovenia, 3, 0.1%
Slovenia
3 publications, 0.1%
|
Tunisia
|
Tunisia, 3, 0.1%
Tunisia
3 publications, 0.1%
|
Armenia
|
Armenia, 2, 0.07%
Armenia
2 publications, 0.07%
|
Indonesia
|
Indonesia, 2, 0.07%
Indonesia
2 publications, 0.07%
|
Mongolia
|
Mongolia, 2, 0.07%
Mongolia
2 publications, 0.07%
|
New Zealand
|
New Zealand, 2, 0.07%
New Zealand
2 publications, 0.07%
|
Saudi Arabia
|
Saudi Arabia, 2, 0.07%
Saudi Arabia
2 publications, 0.07%
|
Turkey
|
Turkey, 2, 0.07%
Turkey
2 publications, 0.07%
|
South Africa
|
South Africa, 2, 0.07%
South Africa
2 publications, 0.07%
|
Portugal
|
Portugal, 1, 0.03%
Portugal
1 publication, 0.03%
|
Austria
|
Austria, 1, 0.03%
Austria
1 publication, 0.03%
|
Bulgaria
|
Bulgaria, 1, 0.03%
Bulgaria
1 publication, 0.03%
|
Hungary
|
Hungary, 1, 0.03%
Hungary
1 publication, 0.03%
|
Vietnam
|
Vietnam, 1, 0.03%
Vietnam
1 publication, 0.03%
|
Greece
|
Greece, 1, 0.03%
Greece
1 publication, 0.03%
|
Denmark
|
Denmark, 1, 0.03%
Denmark
1 publication, 0.03%
|
Iran
|
Iran, 1, 0.03%
Iran
1 publication, 0.03%
|
Ireland
|
Ireland, 1, 0.03%
Ireland
1 publication, 0.03%
|
Romania
|
Romania, 1, 0.03%
Romania
1 publication, 0.03%
|
Serbia
|
Serbia, 1, 0.03%
Serbia
1 publication, 0.03%
|
Croatia
|
Croatia, 1, 0.03%
Croatia
1 publication, 0.03%
|
Czech Republic
|
Czech Republic, 1, 0.03%
Czech Republic
1 publication, 0.03%
|
Sri Lanka
|
Sri Lanka, 1, 0.03%
Sri Lanka
1 publication, 0.03%
|
Ethiopia
|
Ethiopia, 1, 0.03%
Ethiopia
1 publication, 0.03%
|
Show all (22 more) | |
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500
600
700
800
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Publishing countries in 5 years
20
40
60
80
100
120
140
160
|
|
China
|
China, 148, 38.54%
China
148 publications, 38.54%
|
USA
|
USA, 59, 15.36%
USA
59 publications, 15.36%
|
Japan
|
Japan, 59, 15.36%
Japan
59 publications, 15.36%
|
Republic of Korea
|
Republic of Korea, 55, 14.32%
Republic of Korea
55 publications, 14.32%
|
Germany
|
Germany, 13, 3.39%
Germany
13 publications, 3.39%
|
United Kingdom
|
United Kingdom, 11, 2.86%
United Kingdom
11 publications, 2.86%
|
Belgium
|
Belgium, 5, 1.3%
Belgium
5 publications, 1.3%
|
India
|
India, 4, 1.04%
India
4 publications, 1.04%
|
Canada
|
Canada, 4, 1.04%
Canada
4 publications, 1.04%
|
Netherlands
|
Netherlands, 4, 1.04%
Netherlands
4 publications, 1.04%
|
Belarus
|
Belarus, 2, 0.52%
Belarus
2 publications, 0.52%
|
Australia
|
Australia, 2, 0.52%
Australia
2 publications, 0.52%
|
Spain
|
Spain, 2, 0.52%
Spain
2 publications, 0.52%
|
Italy
|
Italy, 2, 0.52%
Italy
2 publications, 0.52%
|
Malaysia
|
Malaysia, 2, 0.52%
Malaysia
2 publications, 0.52%
|
Switzerland
|
Switzerland, 2, 0.52%
Switzerland
2 publications, 0.52%
|
France
|
France, 1, 0.26%
France
1 publication, 0.26%
|
Indonesia
|
Indonesia, 1, 0.26%
Indonesia
1 publication, 0.26%
|
Norway
|
Norway, 1, 0.26%
Norway
1 publication, 0.26%
|
Serbia
|
Serbia, 1, 0.26%
Serbia
1 publication, 0.26%
|
Turkey
|
Turkey, 1, 0.26%
Turkey
1 publication, 0.26%
|
Croatia
|
Croatia, 1, 0.26%
Croatia
1 publication, 0.26%
|
Sri Lanka
|
Sri Lanka, 1, 0.26%
Sri Lanka
1 publication, 0.26%
|
South Africa
|
South Africa, 1, 0.26%
South Africa
1 publication, 0.26%
|
20
40
60
80
100
120
140
160
|
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