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Fig. 1 Projected future changes for Ixodes ricinus until 2081–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 1 Projected future changes for Ixodes ricinus until 2081–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red:
Fig. 5 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 5 Potential co-occurrence under current and future climatic conditions. a Under near current climatic conditions (1970–2000). b Under projected future climatic conditions (exemplarily for SSP 245) for the period 2041–2060. c Under projected future climatic conditions (SSP 245) for the period 2081–2100. Colors indicate areas where climatic suitability is projected for the respective species; for non-mentioned species ("none of them"), the area is climatically unsuitable according to the modelling results. The thresholds to transform the logistic model output (10% omission rate threshold) are as follows: 0.3368 for Ixodes ricinus, 0.3816 for Dermacentor reticulatus, and 0.4298 for D. marginatus. Maps were built using ESRI Arc-GIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic. (A hatch-based version of this figure is additionally provided in the Supplementary Material: Figure S11.)
Fig. 3 Projected future changes for Dermacentor marginatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 3 Projected future changes for Dermacentor marginatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red: area projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). AUC = 0.8229 (average over 10 replicates using cross-validation, standard deviation = 0.001121953). Threshold to transform the logistic model output: 0.4298 (10% omission rate threshold). Maps were built using ESRI ArcGIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic
Fig. 3 in New record of a blood-feeding terrestrial leech, Haemadipsa rjukjuana Oka, 1910 (Haemadipsidae, Arhynchobdellida) on Heuksando Island and possible habitat estimation in the current and future Korean Peninsula using a Maxent model
Fig. 3. Current (A and F) and future distribution models (B-E, G-J) for Haemadipsa rjukjuana in Korea. Dark gray represents over 0.5 MaxEnt value (suitable habitat) and light gray represents below 0.5 (unsuitable habitat). A is projected to the current climate conditions (2020), and F was built with the restricted spatial area between Heuksando Island and Gageodo Island. B-E are projections of the Maxent model to SSP585 of GISS-E2-1 climate scenarios by NASA and G-J were SSP585 of INM-CM4-8 scenarios by The Institute of Numerical Mathematics. B-E and G-J are respectively 2040, 2060, 2080, and 2100.
Fig. 2 in New record of a blood-feeding terrestrial leech, Haemadipsa rjukjuana Oka, 1910 (Haemadipsidae, Arhynchobdellida) on Heuksando Island and possible habitat estimation in the current and future Korean Peninsula using a Maxent model
Fig. 2. Projection of MaxEnt Haemadipsa rjukjuana distribution model from Heuksando Island and Gageodo Island to the current climate condition of South Korea. Red color (lower value) represents less suitable habitats and blue (higher value close to 1.0) represents suitable habitats for H. rjukjuana.
Fig. 1 in New record of a blood-feeding terrestrial leech, Haemadipsa rjukjuana Oka, 1910 (Haemadipsidae, Arhynchobdellida) on Heuksando Island and possible habitat estimation in the current and future Korean Peninsula using a Maxent model
Fig. 1. The map of study sites (inset) and the Korean Peninsula. Haemadipsa rjukjuana was identified from the regions shaded in gray.
Artificial Intelligence and the Future of Smart Cities-Figure 2. Traditional growth model vs. adapted growth model Source: Adapted after Purdy & Daugherty, 2016
<p>The use of AI is not limited to smart buildings or transportation. It covers a wide range of application from medical diagnosis, to robot control and virtual assistance scientific tools. Nowadays, AI can be encountered in many services such as: cars speech recognitions, industrial robots, intelligent vacuum cleaners or fridges and so further. It can also be used in smart homes which permits by using hundreds or even thousands of sensors to provide services according to our preferences such as: ambient assisted living, energy saving etc. According to Skouby et al. (2014), AI also can be utilized in smart homes by adding personalized features in form of context awareness which allows AI to move beyond automation level. These authors designed a four-layer pyramid which encases the ICTs based infrastructure for future smart cites (Figure 3).</p>
Artificial Intelligence and the Future of Smart Cities-Table 1. Smart city complex system factors
<p>Figure 2 underlines the importance of traditional production factors such as capital and labour in achieving and driving growth which arises when either stock of capital or labour increase or they are more effectively used. Total factor productivity (TFP) represents the growth enhanced by the use of technology and innovation. Besides these traditional factors, Purdy and Daugherty (2016) consider that AI can be seen as a new production factor, a capital-labour hybrid that will lead to significant growth opportunities. This is due to the advancement made in AI, which allows nowadays to replicate some labour activities at a greater and faster scale than humans (e.g. virtual text assistance, self-learning machines) (Purdy and Daugherty, 2016).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 1. Smart people, smart ICTs and smart cities
<p>According to other authors “water, sewer, transportation, electricity, telecommunications, housing, healthcare, education — all of these functions—will have to be built from the ground up” (Glasmeier & Christopherson, 2015). This search is facilitated by the evolution of ICTs in general and of AI in particular. AI offers possibilities to replace the human being in complex and dangerous activities. But, smart cities start from smart human capital (Shapiro, 2006; Holland, 2008), because only smart people can create smart ICTs equipped with AI (Figure 1). These people and technologies will solve, by creativity and cooperation, problems associated with urban agglomerations, pollution, the depletion of some natural resources etc.</p>
Artificial Intelligence and the Future of Smart Cities-Figure 9. AI influence on the environment
<p>The participants consider that AI development will increase the energy consumption and e- waste (M=3.60, SD=1.03), but will improve also the level of citizens’ information on the environmental changes (M=3.60, SD=.49). The contribution to CO2 emissions is on the fourth places (M=3.40, SD=.49), followed by the attracting of the community members to environmental actions (M=3.00, SD=.64). In the analysis of the statically significant differences by gender, female participants scored significantly higher (M=4.00, SD=.64) than male participants (M=3.63, SD=.77) in the case of the information of citizens on the environmental changes (M=3.80, SD=.75 vs. M=3.72, SD=.75) and the attracting of community members to environmental actions (M=3.00, SD=.90 vs. M=2.63, SD=.88). The analysis on age category, the results revealed that the 26-30 age group scored the highest at both questions (Figure 9).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 4. Influence of AI in the development of smart cities by respondents age
<p>Respondents were asked to indicate how they evaluate the influence of AI in the development of intelligent cities. In order to fully understand the concept of smart cities, the definition of smart city given by Caragliu (2009) was given to the respondents. It is presented in section 2. On question 6 two-way analysis was used to determine the difference by age and gender. There was no statistically significant interaction between groups as determined by two-way ANOVA F (3, 106) = 8.675, p value>0.05 (p=.387). The assumption of homogeneity of variance was tested using the Brown-Forsythe Test. There were statistically significant differences by gender (F=2.169, p<0.05) and by age (F=30.885, p<0.05). More than 9 in ten (almost 94%) consider AI to be important (50%) or very important (43.8%) while just a few (6.2%) recall a moderate importance for smart cities development. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=4.27, SD=.62) on question 6 „Generally speaking, how do you assess the influence of AI in the development of intelligent cities”. At the same question: the 41-50 age group scored the highest score followed by the 18-25 age group (M=4.40, SD=.49). The 26-30 age group scored lower than the 18-25 age group (M=4.37, SD=.48) and significantly higher than the 31-40 age group (Figure 4).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 5. Smart features as the main beneficiaries of AI in terms of the respondent's age (statistically significant differences only for 7.1 and 7.3)
<p>The majority of the respondents who found the smart features to be the main beneficiaries of AI facilities were ranging between 31-40 years old and +41 age old, followed by the 18-25 age group (M=3.80, SD =0.75), 26-30 (MD=4.0, SD =.75) (Figure 5).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 3. ICT-based infrastructure and its four layers Source: adapted after Skouby et al., 2014
<p>Respondents were asked to indicate how they evaluate the influence of AI in the development of intelligent cities. In order to fully understand the concept of smart cities, the definition of smart city given by Caragliu (2009) was given to the respondents. It is presented in section 2. On question 6 two-way analysis was used to determine the difference by age and gender. There was no statistically significant interaction between groups as determined by two-way ANOVA F (3, 106) = 8.675, p value>0.05 (p=.387). The assumption of homogeneity of variance was tested using the Brown-Forsythe Test. There were statistically significant differences by gender (F=2.169, p<0.05) and by age (F=30.885, p<0.05). More than 9 in ten (almost 94%) consider AI to be important (50%) or very important (43.8%) while just a few (6.2%) recall a moderate importance for smart cities development. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=4.27, SD=.62) on question 6 „Generally speaking, how do you assess the influence of AI in the development of intelligent cities”. At the same question: the 41-50 age group scored the highest score followed by the 18-25 age group (M=4.40, SD=.49). The 26-30 age group scored lower than the 18-25 age group (M=4.37, SD=.48) and significantly higher than the 31-40 age group (Figure 4).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 8. Influence of AI on individual safety by respondents age
<p>On question 11 respondents were asked to consider the influence of AI on individual safety using a 5 points Likert Scale ranging from 1 being “totally unimportant” and 5 being “very important”. The analysis of variance was used to determine the differences by age group and by gender. The analysis shows that no statistical interaction was found between age and gender F=3.081, p=0.08. We found statistically significant differences by gender F=7.639, p<0.05 and age F=6.318, p=.001). Female participants scored significantly higher (M=4.40, SD=.81) than male participants (M=4.00, SD=.60) on question 11 about the influence of AI on individual safety. At the same question: the 41-50 age group scored the highest (M=4.50, SD=.51), followed by the 18-25 age group (M=4.40, SD=.81); the 26-30 age group scored lower than the 18-25 age group and significantly higher than the 31-40 age group (M=3.87, SD=.60) (Figure 8).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 7. Q.9.Which of the following job functions will AI impact the most over the next 10 years? (Statistically significant differences by age for 9.3, 9.4, 9.5 and 9.6)
<p>The analysis reveals that people perceive that AI will have a greater impact over the next 10 years on marketing (for example, intelligent customer targeting, planning and executing marketing campaigns) scored significantly higher (M=4.37, SD=.69) than on finance (for example, robotic financial advisors, automated corporate financial analysis) (M=3.87, SD=.60) (Figure 7). For the same question customer services scored significantly higher (M=3.75, SD=.83) than health (e.g. consultation and diagnosis, surgery) (M=3.25, SD=.83). For the same question, the analyses by gender reveals that the majority of female participants scored significantly higher (M=3.40, SD=.81) than male participants (M=3.18, SD=.57) and those aged in the second group.</p>
Artificial Intelligence and the Future of Smart Cities-Figure 10. Respondents' opinions about the use of robots in different activities (grouped by age)
<p>Question 14 was used to evaluate the respondents’ opinions about the use of robots in the following activities: performing medical surgeries, child care, supply of consumer goods, driving a car, assistance in performing tasks at work and cleaning (Figure 10). The respondents feel most confident and safe to use robots for cleaning (M=4.18, SD=1.07) and for assistance in performing tasks at work (M=4.12, SD=1.05) and less confident and safe to use robots for driving a car (M=3.87, SD=1.11), for supply of consumer goods (M=3.81, SD=.81) and performing medical surgeries (M=3.68, SD=.92) The child care obtained the lower score (M=2.00, SD=86).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 6. Importance of AI for respondents business or industry by respondent's age
<p>On question 8 respondents have to indicate on a scale of 1 to 5 (1 being “not important” and 5 being “critical for survival”), how important they think the AI will be for their business or industry (or for the one they are preparing for) in the next 10 years? No statistical interaction was found between age and gender F (3, 108) = .174, p>0.05). There were statistically significant differences by gender F (1, 108) = 50.261, p<0.05 and age, F (3, 108) = 9.298, p<0.05. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=3.36, SD=.88) on question 8 about the importance of AI for the fields of activity of the participants (or for those they are preparing for) in the next 10 years. At the same question: the 26-30 age group scored the highest followed by the 18-25 age group (M=3.80, SD=1.18); the 31-40 age group scored lower than the 18-25 age group (M=3.50, SD=.87); the 31-40 age group scored also lower than the 18-25 age group (M=3.50, SD=.51) (Figure 6).</p>
Annual minima and maxima of present day and future discharge, derived from PCR-GLOBWB
<p>Global dataset of annual minima and maxima for major river system in the world. Dataset provides the present day and 2C warming simulation derived from a combination of the Global Hydrological Model, PCR-GLOBWB (https://doi.org/10.5194/gmd-11-2429-2018) and the EC-EARTH Global Climate Model (https://doi.org/10.1007/s00382-011-1228-5). The large-ensemble dataset is part of the HiWAVES3 project (https://www.knmi.nl/research/weather-climate-models/projects/hiwaves3)</p>
District heating modelling data for the publication "Integration of feed flow temperatures in unit commitment models of future district heating systems"
<p>Modelling data for a district heating system model which has been used for the publication "Integration of feed flow temperatures in unit commitment models of future district heating systems" on the 4th Generation District Heating (4GDH) conference 2018.</p>
Dataset and replication information for Temporal Discounting in Technical Debt: How do Software Practitioners Discount the Future?
<p>Dataset and replication information for the paper Temporal Discounting in Technical Debt: How do Software Practitioners Discount the Future? (Becker, C., Fagerholm, F., Mohanani, R., Chatzigeorgiou, A., 2009). The dataset consists of answers to a questionnaire on temporal discounting in a technical debt context. The respondents are from two companies. The raw data from the two companies is given separately. Information showing how to replicate the calculations required for analysis is given for each company. The dataset and replication information are given in both Excel (xlsx) and Open Document (ods) formats.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.