Month: March 2024

Evidence from temperature analog for traditional and economic cities in Nigeria: implications for sustainable city-related actions

Latest Results

Abstract

Responding to the threats of climate change by cities requires taking relevant actions that will communicate future conditions in reliable and effective manner for sustainable and transformational climate actions. We used the analog approach to assess the geographical shifts and changes in average temperature conditions for six traditional and economic cities under different climate scenarios (Mitigated and Unmitigated scenarios). We calculated the similarity in temperature between each pixel for the current (2021–2050) and future (2041–2070) conditions of the cities, with every pixel globally in the historical (1971–200) period. Our analysis revealed that; (1) the temperature of the cities in the current and future periods will be similar to conditions of another place on the globe during the historical period; (2) Kano city will experience even more drastic changes because of the low level of similarity to other places; (3) the new places found with similar temperature conditions are generally to the south of the corresponding cities thus indicating warming. The overall results show that the analogues of the cities are within the domain of the global tropical zone which occurs around the equator. Drawing from the interaction between cities and their analogues, we highlighted sustainable city related actions such as the incorporation of urban designs and policies to enhance human thermal comfort as adaptation and solution strategies. While future research might apply qualitative studies and additional data to support the analog results, our findings can guide the understanding and application of the analog approach into environmental issues in Nigeria and other West African countries in accordance to sustainable city goals (SDG 11).

Evidence from temperature analog for traditional and economic cities in Nigeria: implications for sustainable city-related actions

Latest Results

Abstract

Responding to the threats of climate change by cities requires taking relevant actions that will communicate future conditions in reliable and effective manner for sustainable and transformational climate actions. We used the analog approach to assess the geographical shifts and changes in average temperature conditions for six traditional and economic cities under different climate scenarios (Mitigated and Unmitigated scenarios). We calculated the similarity in temperature between each pixel for the current (2021–2050) and future (2041–2070) conditions of the cities, with every pixel globally in the historical (1971–200) period. Our analysis revealed that; (1) the temperature of the cities in the current and future periods will be similar to conditions of another place on the globe during the historical period; (2) Kano city will experience even more drastic changes because of the low level of similarity to other places; (3) the new places found with similar temperature conditions are generally to the south of the corresponding cities thus indicating warming. The overall results show that the analogues of the cities are within the domain of the global tropical zone which occurs around the equator. Drawing from the interaction between cities and their analogues, we highlighted sustainable city related actions such as the incorporation of urban designs and policies to enhance human thermal comfort as adaptation and solution strategies. While future research might apply qualitative studies and additional data to support the analog results, our findings can guide the understanding and application of the analog approach into environmental issues in Nigeria and other West African countries in accordance to sustainable city goals (SDG 11).

Boosting deep neural networks with geometrical prior knowledge: a survey

Latest Results

Abstract

Deep neural networks achieve state-of-the-art results in many different problem settings by exploiting vast amounts of training data. However, collecting, storing and—in the case of supervised learning—labelling the data is expensive and time-consuming. Additionally, assessing the networks’ generalization abilities or predicting how the inferred output changes under input transformations is complicated since the networks are usually treated as a black box. Both of these problems can be mitigated by incorporating prior knowledge into the neural network. One promising approach, inspired by the success of convolutional neural networks in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations of the problem to solve that affect the output in a predictable way. This promises an increased data efficiency and more interpretable network outputs. In this survey, we try to give a concise overview about different approaches that incorporate geometrical prior knowledge into neural networks. Additionally, we connect those methods to 3D object detection for autonomous driving, where we expect promising results when applying those methods.

Boosting deep neural networks with geometrical prior knowledge: a survey

Latest Results

Abstract

Deep neural networks achieve state-of-the-art results in many different problem settings by exploiting vast amounts of training data. However, collecting, storing and—in the case of supervised learning—labelling the data is expensive and time-consuming. Additionally, assessing the networks’ generalization abilities or predicting how the inferred output changes under input transformations is complicated since the networks are usually treated as a black box. Both of these problems can be mitigated by incorporating prior knowledge into the neural network. One promising approach, inspired by the success of convolutional neural networks in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations of the problem to solve that affect the output in a predictable way. This promises an increased data efficiency and more interpretable network outputs. In this survey, we try to give a concise overview about different approaches that incorporate geometrical prior knowledge into neural networks. Additionally, we connect those methods to 3D object detection for autonomous driving, where we expect promising results when applying those methods.

The Society of Information and the European Citizens’ Perception of Climate Change: Natural or Anthropological Causes

Latest Results

Abstract

The scientific community has reached a consensus on humans’ important role as causative agents of climate change; however, branches of society are still sceptical about this. Climate change is a key issue for humanity and only the commitment to change human attitudes and lifestyles, at the global level, can be effective in its mitigation. With this purpose, it is important to convey the right message and prevent misinformation to manipulate people’s minds. The present study aims to understand the factors shaping European citizens’ thoughts on the causes of climate change. Using data from the European Social Survey 10 collected in 2022, we fitted statistical models using the people’s thoughts on causes of climate change (natural, anthropogenic or both) as dependent variables. As independent variables, we used the impact of the media through time spent on news and time spent on the internet, level of education, level of trust in scientists, awareness of online or mobile misinformation and gender. We concluded that the typical European citizen who believes in anthropogenic causes of climate change is a female, is more literate, trusts more in scientists, is younger, spends more time reading the news and has more awareness of misinformation presence in online and mobile communications.

Creationism and climate skepticism: power and public understandings of science in America

Latest Results

Abstract

This FORUM article is written in response to ‘Evolutionary Stasis: creationism, evolution and climate change in the Accelerated Christian Education curriculum’ by Jenna Scaramanga and Michael J. Reiss published in CSSE in 2023. Starting from a sociological rather than pedagogical standpoint, the article aims to situate Accelerated Christian Education’s curriculum in relation to evolution and climate change in its broader context. This broader context comprises a national situation of Culture Wars where views on science and religion are politically polarized and morally inflected. Creationism and climate change denial/skepticism occur together and connect to right-wing politics. Climate change denial also clearly connects to corporate interests. Struggles for political, economic, ideological, and epistemic power all pertain. Reference is then made to recently collected focus group data to illustrate how non-creationist publics may also define science narrowly and inaccurately and yet still support it. The influence of evolution and climate change denialists must not be overstated. However, the harm of inaccurate, pseudoscientific education also requires examination. Nothing less than the Earth’s future is at stake, and education is a key battlefield. Science educators have an important role to play, working with patience, empathy, and awareness.

Developing climate change adaptation pathways in the agricultural sector based on robust decision-making approach (case study: Sefidroud Irrigation Network, Iran)

Latest Results

Abstract

Allocation of water in the situation of climate change presents various uncertainties. Consequently, decisions must be made to ensure stability and functionality across different climatic scenarios. This study aims to examine the effectiveness of adaptation strategies in the agricultural sector, including a 5% increase in irrigation efficiency (S1) and a shift in irrigation method to Dry-DSR (direct seeded rice) under conditions of climatic uncertainty using a decision-making approach. The study focuses on the basin downstream of the Sefidroud dam, encompassing the Sefidroud irrigation and drainage network. Initially, basin modeling was conducted using the WEAP integrated management software for the period 2006–2020. Subsequently, the impact of climate change was assessed, considering RCP2.6, RCP4.5, and RCP8.5 emission scenarios on surface water resources from 2021 to 2050. Runoff and cultivated area, both subject to uncertainty, were identified as key parameters. To evaluate strategy performance under different uncertainties and determine the efficacy of each strategy, regret and satisfaction approaches were employed. Results indicate a projected decrease in future rainfall by 3.5–11.8% compared to the base period, accompanied by an increase in maximum and minimum temperatures (0.83–1.62 °C and 1.15–1.33 °C, respectively). Inflow to the Sefidroud dam is expected to decrease by 13–28%. Presently, the Sefidroud irrigation and drainage network faces an annual deficit of 505.4 MCM, and if current trends persist with the impact of climate change, this shortfall may increase to 932.7 MCM annually. Furthermore, satisfaction indices for strategy (S2) are 0.77 in an optimistic scenario and 0.70 in strategy (S1). In a pessimistic scenario, these indices are 0.67 and 0.56, respectively. Notably, changing the irrigation method with Dry-DSR is recommended as a robust strategy, demonstrating the ability to maintain basin stability under a broad range of uncertainties and climate change scenarios. It is crucial to note that the results solely highlight the effects of climate change on water sources entering the Sefidroud dam. Considering anthropogenic activities upstream of the Sefidroud basin, water resource shortages are expected to increase. Therefore, reallocating water resources and implementing practical and appropriate measures in this area are imperative.