The global concentration of fine particulate matter (PM2.5) is experiencing an upward trend. This study investigates the utilization of space-time cubes to visualize and interpret PM2.5 data in South Africa over multiple temporal intervals spanning from 1998 to 2022. The findings indicated that the mean PM2.5 concentrations in Gauteng Province were the highest, with a value of 53 μg/m3 in 2010, whereas the lowest mean PM2.5 concentrations were seen in the Western Cape Province, with a value of 6.59 μg/m3 in 1999. In 2010, there was a rise in the average concentration of PM2.5 across all provinces. The increase might be attributed to South Africa being the host nation for the 2010 FIFA World Cup. In most provinces, there has been a general trend of decreasing PM2.5 concentrations over the previous decade. Nevertheless, the issue of PM2.5 remains a large reason for apprehension. The study also forecasts South Africa's PM2.5 levels until 2029 using simple curve fitting, exponential smoothing and forest-based models. Spatial analysis revealed that different areas require distinct models for accurate forecasts. The complexity of PM2.5 trends underscores the necessity for varied models and evaluation tools.
Citation: Tabaro H. Kabanda. Investigating PM2.5 pollution patterns in South Africa using space-time analysis[J]. AIMS Environmental Science, 2024, 11(3): 426-443. doi: 10.3934/environsci.2024021
[1] | Liandi Fang, Li Ma, Shihong Ding . Finite-time fuzzy output-feedback control for -norm stochastic nonlinear systems with output constraints. AIMS Mathematics, 2021, 6(3): 2244-2267. doi: 10.3934/math.2021136 |
[2] | Yanghe Cao, Junsheng Zhao, Zongyao Sun . State feedback stabilization problem of stochastic high-order and low-order nonlinear systems with time-delay. AIMS Mathematics, 2023, 8(2): 3185-3203. doi: 10.3934/math.2023163 |
[3] | Yihang Kong, Xinghui Zhang, Yaxin Huang, Ancai Zhang, Jianlong Qiu . Prescribed-time adaptive stabilization of high-order stochastic nonlinear systems with unmodeled dynamics and time-varying powers. AIMS Mathematics, 2024, 9(10): 28447-28471. doi: 10.3934/math.20241380 |
[4] | Lu Zhi, Jinxia Wu . Adaptive constraint control for nonlinear multi-agent systems with undirected graphs. AIMS Mathematics, 2021, 6(11): 12051-12064. doi: 10.3934/math.2021698 |
[5] | Yankui Song, Bingzao Ge, Yu Xia, Shouan Chen, Cheng Wang, Cong Zhou . Low-cost adaptive fuzzy neural prescribed performance control of strict-feedback systems considering full-state and input constraints. AIMS Mathematics, 2022, 7(5): 8263-8289. doi: 10.3934/math.2022461 |
[6] | Jingjing Yang, Jianqiu Lu . Stabilization in distribution of hybrid stochastic differential delay equations with Lévy noise by discrete-time state feedback controls. AIMS Mathematics, 2025, 10(2): 3457-3483. doi: 10.3934/math.2025160 |
[7] | Wei Zhao, Lei Liu, Yan-Jun Liu . Adaptive neural network control for nonlinear state constrained systems with unknown dead-zones input. AIMS Mathematics, 2020, 5(5): 4065-4084. doi: 10.3934/math.2020261 |
[8] | Kunting Yu, Yongming Li . Adaptive fuzzy control for nonlinear systems with sampled data and time-varying input delay. AIMS Mathematics, 2020, 5(3): 2307-2325. doi: 10.3934/math.2020153 |
[9] | Changgui Wu, Liang Zhao . Finite-time adaptive dynamic surface control for output feedback nonlinear systems with unmodeled dynamics and quantized input delays. AIMS Mathematics, 2024, 9(11): 31553-31580. doi: 10.3934/math.20241518 |
[10] | Zhaohui Chen, Jie Tan, Yong He, Zhong Cao . Decentralized observer-based event-triggered control for an interconnected fractional-order system with stochastic Cyber-attacks. AIMS Mathematics, 2024, 9(1): 1861-1876. doi: 10.3934/math.2024091 |
The global concentration of fine particulate matter (PM2.5) is experiencing an upward trend. This study investigates the utilization of space-time cubes to visualize and interpret PM2.5 data in South Africa over multiple temporal intervals spanning from 1998 to 2022. The findings indicated that the mean PM2.5 concentrations in Gauteng Province were the highest, with a value of 53 μg/m3 in 2010, whereas the lowest mean PM2.5 concentrations were seen in the Western Cape Province, with a value of 6.59 μg/m3 in 1999. In 2010, there was a rise in the average concentration of PM2.5 across all provinces. The increase might be attributed to South Africa being the host nation for the 2010 FIFA World Cup. In most provinces, there has been a general trend of decreasing PM2.5 concentrations over the previous decade. Nevertheless, the issue of PM2.5 remains a large reason for apprehension. The study also forecasts South Africa's PM2.5 levels until 2029 using simple curve fitting, exponential smoothing and forest-based models. Spatial analysis revealed that different areas require distinct models for accurate forecasts. The complexity of PM2.5 trends underscores the necessity for varied models and evaluation tools.
AIMS Energy is an Open Access international journal devoted to publishing peer-reviewed, high quality, original papers in the field of Energy science and technology, to promote the worldwide better understanding of full spectra of energy issues. Together with the Editorial Office of AIMS Energy, I wish to testify my sincere gratitude to all authors, members of the editorial board, and peer reviewers for their contribution to AIMS Energy in 2022.
In 2022, we had received 247 manuscripts, of which 58 have been accepted and published. These published papers include 40 research articles, 12 review articles, 4 editorial, and 2 opinion papers. The authors of the manuscripts are from more than 35 countries worldwide. The sources of the submissions showed a significant increase in international collaborations on the research of Energy technologies.
One of the important strategies of attracting high quality and high impact papers to our journal has been the calls for special issues. In 2022, 10 special issues were planned and called, and four of which have already published five high quality articles so far. Currently, there are 10 special issues open, and we expect to collect excellent articles for publication. AIMS Energy has 102 enthusiastic members on the editorial board, and 16 of them just joined in 2022. We will continue to renew and accept dedicated researchers to join the Editorial Board in 2023. Our members are active researchers, and we are confident that, with their dedicated effort, the journal will offer our readers more impactful publications.
With the high demand on innovative research for renewable energy and efficient utilization of energy, we expect to receive and collect more excellent articles being submitted to AIMS Energy in 2023. We would also like our members of the editorial board to encourage more of the peer researchers to publish papers and support AIMS Energy. The journal will dedicate to publishing high quality papers by both regular issues and special issues organized by the members of the editorial board. We believe that all these efforts will increase the impact and citations of the papers published by AIMS Energy.
Wish the best 2023 to our dedicated editorial board members, authors, peer reviewers, and staff members of the Editorial Office of AIMS Energy.
Prof. Peiwen (Perry) Li, Editor in Chief
AIMS Energy
Dept. of Aerospace and Mechanical Engineering,
University of Arizona, USA
The three-year manuscript statistics are shown below. In 2022, AIMS Energy published 6 issues, a total of 58 articles were published online, and the categories of published articles are as follows:
Type | Number |
Research Article | 40 |
Review | 12 |
Editorial | 4 |
Opinion Paper | 2 |
![]() |
Peer Review Rejection rate: 56%
Publication time (from submission to online): 90 days
An important part of our strategy of attracting high quality papers has been the call and preparation of special issues. In 2022, ten special issues were called by the editoral board members. Listed below are some examples of issues that have more than 5 papers. We encourage Editorial Board members to propose more potential topics, and to act as editors of special issues.
Title | Link | Number of published |
Analyzing energy storage systems for the applications of renewable energy sources | https://www.aimspress.com/aimse/article/6042/special-articles | 6 |
Current Status and Future Prospects of Biomass Energy | https://www.aimspress.com/aimse/article/5988/special-articles | 5 |
Hybrid renewable energy system design | https://www.aimspress.com/aimse/article/6313/special-articles | 5 |
AIMS Energy has Editorial Board members representing researchers from 23 countries, which are shown below.
We are constantly assembling the editorial board to be representative to a variety of disciplines across the field of Energy. AIMS Energy has 102 members now, and 16 of them joined in 2022. We will continue to invite dedicated experts and researchers, in order to renew the Editorial Board in 2023.
![]() |
In the last few years, our journal has developed much faster than before; we received more than 200 manuscript submissions and published 58 papers in 2022. We have added 16 new Editorial Board members, and called for 10 special issues in 2022.
The growth of our journal will require us to invite dedicated and prestigious researchers and experts to our Editorial Board. Keeping this in mind, our first action in 2023 is to renew and rotate members of the Editorial Board. The foremost task is to invite more high-quality articles (Research and Review); especially the review articles to provide readers broad views on the technologies that interest us. We would like to increase the diversity of high-quality articles from all around the world. To set a goal, we would like to publish 60 high-quality articles in 2023. We hope Editorial Board members could help us invite some reputational scholars in your network and field to contribute articles to our journal.
Lastly, we would like to invite our board members to try to increase the influence and impact of AIMS Energy by soliciting and advertising high quality articles and special issues.
We really appreciate the time and effort of all our Editorial Board Members and Guest Editors, as well as our reviewers devoted to our journal in the difficult circumstances we have had in the last three years. All your excellent professional effort and expertise has provided us with very useful and professional suggestions in 2022. Last, but not least, thanks are given to the hard work of the in-house editorial team.
[1] |
Katoto PDMC, Byamungu L, Brand AS, et al. (2019) Ambient air pollution and health in Sub-Saharan Africa: Current evidence, perspectives and a call to action. Environ Res 173: 174–188. https://doi.org/10.1016/j.envres.2019.03.029 doi: 10.1016/j.envres.2019.03.029
![]() |
[2] |
Edlund KK, Killman F, Molnár P, et al. (2021) Health risk assessment of PM2.5 and PM2.5-bound trace elements in Thohoyandou, South Africa. Int J Environ Res 18: 1359. https://doi.org/10.3390/ijerph18031359 doi: 10.3390/ijerph18031359
![]() |
[3] | Indoor Quality Air. Air quality in South Africa, 2022. Available from: https://www.iqair.com/south-africa. |
[4] | Zulu T, Aphane O, Audat T, et al. (2019) South Africa energy sector report. Available from: http://www.energy.gov.za/files/media/explained/2019-South-African-Energy-Sector-Report.pdf. |
[5] |
Zhang R, Di B, Luo Y, et al. (2018) A nonparametric approach to filling gaps in satellite-retrieved aerosol optical depth for estimating ambient PM2.5 levels. Environ Pollut 243: 998–1007. https://doi.org/10.1016/j.envpol.2018.09.052 doi: 10.1016/j.envpol.2018.09.052
![]() |
[6] |
Yan JW, Tao F, Zhang SQ, et al. (2021) Spatiotemporal distribution characteristics and driving forces of PM2.5 in three urban agglomerations of the Yangtze River Economic Belt. Int J Env Res Pub He 18: 2222. https://doi.org/10.3390/ijerph18052222 doi: 10.3390/ijerph18052222
![]() |
[7] |
Chudnovsky AA, Koutrakis P, Kloog I, et al. (2014) Fine particulate matter predictions using high-resolution aerosol optical depth (AOD) retrievals. Atmos Environ 89: 189–198. https://doi.org/10.1016/j.atmosenv.2014.02.019 doi: 10.1016/j.atmosenv.2014.02.019
![]() |
[8] |
Stowell JD, Bi J, Al-Hamdan MZ, et al. (2020) Estimating PM2.5 in Southern California using satellite data: Factors that affect model performance. Environ Res Lett 15: 094004. https://doi.org/10.1088/1748-9326/ab9334 doi: 10.1088/1748-9326/ab9334
![]() |
[9] |
Hu X, Waller LA, Al-Hamdan MZ, et al. (2013) Estimating ground-level PM2.5 concentrations in the southeastern U.S. using geographically weighted regression. Environ Res 121: 1–10. https://doi.org/10.1016/j.envres.2012.11.003 doi: 10.1016/j.envres.2012.11.003
![]() |
[10] |
Kneen MA, Lary DJ, Harrison WA, et al. (2016) Interpretation of satellite retrievals of PM2.5 over the southern African Interior. Atmos Environ 128: 53–64. https://doi.org/10.1016/j.atmosenv.2015.12.016 doi: 10.1016/j.atmosenv.2015.12.016
![]() |
[11] |
Muyemeki L, Burger R, Piketh SJ (2020) Evaluating the potential of remote sensing imagery in mapping ground-level fine particulate matter (PM25) for the Vaal triangle priority area. Clean Air J 30: 1–7. https://doi.org/10.17159/caj/2020/30/1.8066 doi: 10.17159/caj/2020/30/1.8066
![]() |
[12] |
Hu X, Belle JH, Meng X, et al (2017) Estimating PM2.5 concentrations in the conterminous United States using the random forest approach. Environ Sci Technol 51: 6936–6944. https://doi.org/10.1021/acs.est.7b01210.s001 doi: 10.1021/acs.est.7b01210.s001
![]() |
[13] |
van Donkelaar A, Hammer M, Bindle L, et al. (2021) Monthly global estimates of fine particulate matter and their uncertainty. Environ Sci Technol 55: 15287–15300. https://doi.org/10.1021/acs.est.1c05309 doi: 10.1021/acs.est.1c05309
![]() |
[14] |
Knibbs LD, van Donkelaar A, Martin RV, et al. (2018) Satellite-based land-use regression for continental-scale long-term ambient PM2.5 exposure assessment in Australia. Environ Sci Technol 52: 12445–12455. https://doi.org/10.1021/acs.est.8b02328 doi: 10.1021/acs.est.8b02328
![]() |
[15] |
de Hoogh K, Gulliver J, van Donkelaar A, et al. (2016) Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data. Environ Res 151: 1–10. https://doi.org/10.1016/j.envres.2016.07.005 doi: 10.1016/j.envres.2016.07.005
![]() |
[16] |
Hammer MS, van Donkelaar A, Li C, et al. (2020) Global estimates and long-term trends of fine particulate matter concentrations (1998–2018). Environ Sci Technol 54: 7879–7890. https://doi.org/10.1021/acs.est.0c01764 doi: 10.1021/acs.est.0c01764
![]() |
[17] |
van Donkelaar A, Martin RV, Li C, et al. (2019) Regional estimates of chemical composition of fine particulate matter using a combined geoscience-statistical method with information from satellites, models, and monitors. Environ Sci Technol 53: 2595. https://doi.org/10.1021/acs.est.8b06392 doi: 10.1021/acs.est.8b06392
![]() |
[18] |
Fenderson LE, Kovach AI, Llamas B (2020) Spatiotemporal landscape genetics: Investigating ecology and evolution through space and time. Mol Ecol 29: 218–246. https://doi.org/10.1111/mec.15315 doi: 10.1111/mec.15315
![]() |
[19] | Osman A, Owusu AB, Adu-Boahen K, et al. (2023) Space-time cube approach in analysing conflicts in Africa. Soc Sci Humanit Open 8. https://doi.org/10.1016/j.ssaho.2023.100557 |
[20] |
Yoon J, Lee S (2021) Spatio-temporal patterns in pedestrian crashes and their determining factors: Application of a space-time cube analysis model. Accident Anal Prev 161. https://doi.org/10.1016/j.aap.2021.106291 doi: 10.1016/j.aap.2021.106291
![]() |
[21] |
Allen MJ, Allen TR, Davis C (2021) Exploring spatial patterns of Virginia tornadoes using kernel density and space-time cube analysis (1960–2019). ISPRS Int J Geo-Inf 10: 310. https://doi.org/10.3390/ijgi10050310 doi: 10.3390/ijgi10050310
![]() |
[22] |
Mo C, Tan D, Mai T, et al. (2020) An analysis of spatiotemporal pattern for COVID-19 in China based on space‐time cube. J Med Virol 92: 1587–1595. https://doi.org/10.1002/jmv.25834 doi: 10.1002/jmv.25834
![]() |
[23] | South African Yearbook (2021) South Africa Yearbook 2021/22. Available: https://www.gcis.gov.za/south-africa-yearbook-202122. |
[24] | WUSTL (Washington University in St. Louis) (2022) Atmospheric composition analysis group-surface PM2.5. Available from: https://sites.wustl.edu/acag/datasets/surface-pm2-5/. |
[25] | ESRI (2022) How Emerging Hot Spot Analysis Works. Available from: https://pro.arcgis.com/en/pro-app/latest/tool-reference/space-time-pattern-mining/learnmoreemerging.htm. |
[26] |
Malik A, Kumar A, Pham QB, et al. (2020) Identification of EDI trend using Mann-Kendall and innovative trend methods (Uttarakhand, India). Arab J Geosci 13: 951. https://doi.org/10.1007/s12517-020-05926-2 doi: 10.1007/s12517-020-05926-2
![]() |
[27] |
Cui J, Liu Y, Sun J, et al. (2021) G-STC-M spatiotemporal analysis method for archaeological sites. ISPRS Int J Geo-Inf 10: 312. https://doi.org/10.3390/ijgi10050312 doi: 10.3390/ijgi10050312
![]() |
[28] |
Zhang H, Tripathi NK (2018) Geospatial hot spot analysis of lung cancer patients correlated to fine particulate matter (PM2.5) and industrial wind in Eastern Thailand. J Clean Prod 170: 407–424. https://doi.org/10.1016/j.jclepro.2017.09.185 doi: 10.1016/j.jclepro.2017.09.185
![]() |
[29] |
Harris NL, Goldman C, Gabris J, et al. (2017) Using spatial statistics to identify emerging hot spots of forest loss using spatial statistics to identify emerging hot spots of forest loss. Environ Res Lett 12. https://doi.org/10.1088/1748-9326/aa5a2f doi: 10.1088/1748-9326/aa5a2f
![]() |
[30] |
Wan Y, Beydoun MA (2007) The obesity epidemic in the United States—gender, Age, socioeconomic, racial/ethnic, and geographic characteristics: A systematic review and meta-regression analysis. Epidemiol Rev 29. https://doi.org/10.1093/epirev/mxm007 doi: 10.1093/epirev/mxm007
![]() |
[31] | Barazzetti L, Previtali M, Roncoroni F (2022) Visualisation and processing of structural monitoring data using space-time cubes, International Conference on Computational Science and Its Applications, Springer, Cham. https://doi.org/10.1007/978-3-031-10450-3_2 |
[32] |
Zhou R, Chen H, Chen H, et al. (2021) Research on traffic situation analysis for urban road network through spatiotemporal data mining: A case study of Xi'an, China. IEEE Access 9: 75553–75567. https://doi.org/10.1109/access.2021.3082188 doi: 10.1109/access.2021.3082188
![]() |
[33] |
Cherchi E, Cirillo C (2010) Validation and forecasts in models estimated from multiday travel survey. Transport Res Rec 2175: 57–64. https://doi.org/10.3141/2175-07 doi: 10.3141/2175-07
![]() |
[34] | Arsham H (2020) Time-critical decision-making for business administration. Time Series Ana Bus Forecast. |
[35] | ESRI (2023) Train time series forecasting model. Available from: https://pro.arcgis.com/en/pro-app/latest/tool-reference/geoai/train-time-series-forecasting-model.htm. |
[36] |
Lapere R, Menut L, Mailler S, et al. (2020) Soccer games and record-breaking PM2.5 pollution events in Santiago, Chile. Atmos Chem Phys 20: 4681–4694. https://doi.org/10.5194/acp-20-4681-2020 doi: 10.5194/acp-20-4681-2020
![]() |
[37] | van der Merwe C (2010) The World Cup's 2, 7 MT carbon footprint and what's being done about it. Available from: https://www.engineeringnews.co.za/article/the-world-cups-27-million-ton-carbon-footprint-2010-01-22. |
[38] | Paul M (2022) Different air under one sky: Almost everyone in South Africa breathes polluted air. Available from: https://www.downtoearth.org.in/news/health-in-africa/different-air-under-one-sky-almost-everyone-in-south-africa-breathes-polluted-air-84743. |
[39] | Gray HA (2019) Air quality impacts and health effects due to sizeable stationary source emissions in and around South Africa's Mpumalanga highveld priority area, San Rafael, CA USA: Gray Sky Solutions. |
[40] |
Zhang D, Du L, Wang W, et al. (2021) A machine learning model to estimate ambient PM2.5 concentrations in industrialized highveld region of South Africa. Remote Sens Environ 266: 112713. https://doi.org/10.1016/j.rse.2021.112713 doi: 10.1016/j.rse.2021.112713
![]() |
[41] |
Adeyemi A, Molnar P, Boman J, et al. (2022) Particulate matter (PM2.5) characterization, air quality level and origin of air masses in an urban background in Pretoria. Arch Environ Con Tox 83: 77–94. https://doi.org/10.1007/s00244-022-00937-4 doi: 10.1007/s00244-022-00937-4
![]() |
[42] |
Mollo VM, Nomngongo PN, Ramontja J (2022) Evaluation of surface water quality using various indices for heavy metals in Sasolburg, South Africa. Water 14: 2375. https://doi.org/10.3390/w1415237 doi: 10.3390/w1415237
![]() |
[43] |
Moreoane L, Mukwevho P, Burger R (2021) The quality of the first and second Vaal triangle airshed priority area air quality management plans. Clean Air J 31: 1–14. https://doi.org/10.17159/caj/2020/31/2.12178 doi: 10.17159/caj/2020/31/2.12178
![]() |
[44] |
Scorgie Y, Kneen A, Annegarn HJ, et al. (2003) Air pollution in the Vaal triangle-quantifying source contributions and identifying cost-effective solutions. Clean Air J 13: 5–18. https://doi.org/10.17159/caj/2003/13/2.7152 doi: 10.17159/caj/2003/13/2.7152
![]() |
[45] |
Venter AD, Beukes JP, Van Zyl PG (2012) An air quality assessment in the industrialised western Bushveld Igneous Complex, South Africa. S Afr J Sci 108: 1–10. https://doi.org/10.4102/sajs.v108i9/10.1059 doi: 10.4102/sajs.v108i9/10.1059
![]() |
[46] |
Matandirotya NR, Burger R (2023) An assessment of NO2 atmospheric air pollution over three cities in South Africa during 2020 COVID-19 pandemic. Air Qual Atmos Hlth 16: 263–276. https://doi.org/10.1007/s11869-022-01271-3 doi: 10.1007/s11869-022-01271-3
![]() |
[47] |
Shikwambana L, Mhangara P, Mbatha N (2020) Trend analysis and first time observations of sulphur dioxide and nitrogen dioxide in South Africa using TROPOMI/Sentinel-5 P data. Int J Appl Earth Obs 91. https://doi.org/10.1016/j.jag.2020.102130 doi: 10.1016/j.jag.2020.102130
![]() |
[48] | Department of Environmental Affairs (DEA) (2019) The second generation Vaal triangle airshed priority area air quality management plan: Draft baseline assessment report, Pretoria: DEA. |
[49] | Norman R, Cairncross E, Witi J, et al. (2007) Estimating the burden of disease attributable to urban outdoor air pollution in South Africa in 2000. S Afr Med J 97: 748–753. |
[50] |
Muyemeki L, Burger R, Piketh SJ, et al. (2021) Source apportionment of ambient PM10-25 and PM2.5 for the Vaal triangle, South Africa. S Afr J Sci 117: 1–11. https://doi.org/10.17159/sajs.2021/8617 doi: 10.17159/sajs.2021/8617
![]() |
[51] |
Oosthuizen MA, Mundackal AJ, Wright CY (2014) The prevalence of asthma among children in South Africa is increasing-is the need for medication increasing as well? A case study in the Vaal triangle. Clean Air J 24: 28–30. https://doi.org/10.17159/caj/2014/24/1.7050 doi: 10.17159/caj/2014/24/1.7050
![]() |
[52] |
Liu H, Yan G, Duan Z, et al. (2021) Intelligent modeling strategies for forecasting air quality time series: A review. Appl Soft Comput 102. https://doi.org/10.1016/j.asoc.2020.106957 doi: 10.1016/j.asoc.2020.106957
![]() |
[53] |
Gilliam RC, Hogrefe C, Rao ST (2006) New methods for evaluating meteorological models used in air quality applications. Atmos Environ 40: 5073–5086. https://doi.org/10.1016/j.atmosenv.2006.01.023 doi: 10.1016/j.atmosenv.2006.01.023
![]() |
1. | Helena Sousa, Oscar Ribeiro, Daniela Figueiredo, Purpose in life among haemodialysis caregivers: Links with adaptive coping, caregiver burden, and psychological distress, 2024, 40, 1532-3005, 10.1002/smi.3460 | |
2. | Edicleia Oliveira, Serge Basini, Thomas M. Cooney, Navigating gendered spaces: a feminist phenomenological exploration of women entrepreneurs lived experiences within government support agencies, 2024, 16, 1756-6266, 564, 10.1108/IJGE-10-2023-0258 | |
3. | Thiago Alves, Rogério S. Gonçalves, 2024, Development of a Cable-Driven Robot with a Compensation Measurement System for Bimanual and Cognitive Rehabilitation of Stroke, 1, 10.5753/sbrlars_estendido.2024.243872 | |
4. | Sjors F Van de Vusse, Nienke N De Laat, Lennard A Koster, Bart L Kaptein, The accuracy and precision of CT-RSA in arthroplasty: a systematic review and meta-analysis, 2025, 96, 1745-3682, 10.2340/17453674.2025.43334 | |
5. | Eider Arbizu Fernández, Arkaitz Galbete Jimenez, Tomás Belzunegui Otano, Mariano Fortún Moral, Alfredo Echarri Sucunza, Analysis of serious trauma injury patterns in Navarre (Spain) (2010-2019), 2024, 47, 2340-3527, 10.23938/ASSN.1085 |