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Ozone (O3), a major air pollutant, can negatively impact plant growth and yield. While O3 impacts have been widely documented in crops such as wheat and soybean, few studies have looked at the effects of O3 on sorghum, a C4 plant and the fifth most important cereal crop worldwide. We exposed grain sorghum (Sorghum bicolor cv. HAT150843) to a range of O3 concentrations (daytime mean O3 concentrations ranged between 20 and 97 ppb) in open-top chambers, and examined how whole plant and leaf morphological traits varied in response to O3 exposure. Results showed no significant impact of realistic O3 exposure on whole plant biomass and its partitioning in sorghum. These findings suggest that sorghum is generally resistant to O3 and should be considered as a favourable crop in O3 polluted regions, while acknowledging further research is needed to understand the mechanistic basis of O3 tolerance in sorghum.

Ozone (O3), a major air pollutant, can negatively impact plant growth and yield. While O3 impacts have been widely documented in crops such as wheat and soybean, few studies have looked at the effects of O3 on sorghum, a C4 plant and the fifth most important cereal crop worldwide. We exposed grain sorghum (Sorghum bicolor cv. HAT150843) to a range of O3 concentrations (daytime mean O3 concentrations ranged between 20 and 97 ppb) in open-top chambers, and examined how whole plant and leaf morphological traits varied in response to O3 exposure. Results showed no significant impact of realistic O3 exposure on whole plant biomass and its partitioning in sorghum. These findings suggest that sorghum is generally resistant to O3 and should be considered as a favourable crop in O3 polluted regions, while acknowledging further research is needed to understand the mechanistic basis of O3 tolerance in sorghum.

The transformation of energy markets is at a crossroads in the search for how they must evolve to become ecologically friendly systems and meet the growing energy demand. Currently, methodologies based on bibliographic data analysis are supported by information and communication technologies and have become necessary. More sophisticated processes are being used in energy systems, including new digitalization models, particularly driven by artificial intelligence (AI) technology. In the present bibliographic review, 342 documents indexed in Scopus have been identified that promote synergies between AI and the energy transition (ET), considering a time range from 1990 to 2024. The analysis methodology includes an evaluation of keywords related to the areas of AI and ET. The analyses extend to a review by authorship, co-authorship, and areas of AI’s influence in energy system subareas. The integration of energy resources, including supply and demand, in which renewable energy sources play a leading role at the end-customer level, now conceived as both producer and consumer, is intensively studied. The results identified that AI has experienced notable growth in the last five years and will undoubtedly play a leading role in the future in achieving decarbonization goals. Among the applications that it will enable will be the design of new energy markets up to the execution and start-up of new power plants with energy control and optimization. This study aims to present a baseline that allows researchers, legislators, and government decision-makers to compare their benefits, ambitions, strategies, and novel applications for formulating AI policies in the energy field. The developments and scope of AI in the energy sector were explored in relation to the AI domain in parts of the energy supply chain. While these processes involve complex data analysis, AI techniques provide powerful solutions for designing and managing energy markets with high renewable energy penetration. This integration of AI with energy systems represents a fundamental shift in market design, enabling more efficient and sustainable energy transitions. Future lines of research could focus on energy demand forecasting, dynamic adjustments in energy distribution between different generation sources, energy storage, and usage optimization.

The transformation of energy markets is at a crossroads in the search for how they must evolve to become ecologically friendly systems and meet the growing energy demand. Currently, methodologies based on bibliographic data analysis are supported by information and communication technologies and have become necessary. More sophisticated processes are being used in energy systems, including new digitalization models, particularly driven by artificial intelligence (AI) technology. In the present bibliographic review, 342 documents indexed in Scopus have been identified that promote synergies between AI and the energy transition (ET), considering a time range from 1990 to 2024. The analysis methodology includes an evaluation of keywords related to the areas of AI and ET. The analyses extend to a review by authorship, co-authorship, and areas of AI’s influence in energy system subareas. The integration of energy resources, including supply and demand, in which renewable energy sources play a leading role at the end-customer level, now conceived as both producer and consumer, is intensively studied. The results identified that AI has experienced notable growth in the last five years and will undoubtedly play a leading role in the future in achieving decarbonization goals. Among the applications that it will enable will be the design of new energy markets up to the execution and start-up of new power plants with energy control and optimization. This study aims to present a baseline that allows researchers, legislators, and government decision-makers to compare their benefits, ambitions, strategies, and novel applications for formulating AI policies in the energy field. The developments and scope of AI in the energy sector were explored in relation to the AI domain in parts of the energy supply chain. While these processes involve complex data analysis, AI techniques provide powerful solutions for designing and managing energy markets with high renewable energy penetration. This integration of AI with energy systems represents a fundamental shift in market design, enabling more efficient and sustainable energy transitions. Future lines of research could focus on energy demand forecasting, dynamic adjustments in energy distribution between different generation sources, energy storage, and usage optimization.

Forecasting insect responses to environmental variables at local and global spatial scales remains a crucial task in Ecology. However, predicting future responses requires long-term datasets, which are rarely available for insects, especially in the tropics. From 2002 to 2017, we recorded male ant incidence of 155 ant species at ten malaise traps on the 50-ha ForestGEO plot in Barro Colorado Island. In this Panamanian tropical rainforest, traps were deployed for two weeks during the wet and dry seasons. Short-term changes in the timing of male flying activity were pronounced, and compositionally distinct assemblages flew during the wet and dry seasons. Notably, the composition of these distinct flying assemblages oscillated in consistent 4-year cycles but did not change during the 16-year study period. Across time, a Seasonal Auto-Regressive Integrated Moving Average model explained 75% of long-term variability in male ant production (i.e., the summed incidence of male species across traps), which responded negatively to monthly maximum temperature, and positively to sea surface temperature, a surrogate for El Niño Southern Oscillation (ENSO) events. Establishing these relationships allowed us to forecast ant production until 2022 when year-long local climate variables were available. Consistent with the data, the forecast indicated no significant changes in long-term temporal trends of male ant production. However, simulations of different scenarios of climate variables found that strong ENSO events and maximum temperature impacted male ant production positively and negatively, respectively. Our results highlight the dependence of ant male production on both short- and long-term temperature changes, which is critical under current global warming.

Forecasting insect responses to environmental variables at local and global spatial scales remains a crucial task in Ecology. However, predicting future responses requires long-term datasets, which are rarely available for insects, especially in the tropics. From 2002 to 2017, we recorded male ant incidence of 155 ant species at ten malaise traps on the 50-ha ForestGEO plot in Barro Colorado Island. In this Panamanian tropical rainforest, traps were deployed for two weeks during the wet and dry seasons. Short-term changes in the timing of male flying activity were pronounced, and compositionally distinct assemblages flew during the wet and dry seasons. Notably, the composition of these distinct flying assemblages oscillated in consistent 4-year cycles but did not change during the 16-year study period. Across time, a Seasonal Auto-Regressive Integrated Moving Average model explained 75% of long-term variability in male ant production (i.e., the summed incidence of male species across traps), which responded negatively to monthly maximum temperature, and positively to sea surface temperature, a surrogate for El Niño Southern Oscillation (ENSO) events. Establishing these relationships allowed us to forecast ant production until 2022 when year-long local climate variables were available. Consistent with the data, the forecast indicated no significant changes in long-term temporal trends of male ant production. However, simulations of different scenarios of climate variables found that strong ENSO events and maximum temperature impacted male ant production positively and negatively, respectively. Our results highlight the dependence of ant male production on both short- and long-term temperature changes, which is critical under current global warming.

The exponential growth of the Internet of Things (IoT) has boosted connectivity across various sectors, such as Industry 4.0 and smart cities. However, this expansion has also exposed IoT devices to critical vulnerabilities, including spoofing, DoS attacks, and unauthorized access. Traditional security solutions, based on centralized architectures, are neither scalable nor efficient enough to handle the increasing complexity and number of IoT devices, leading to high latencies, increased energy consumption, and inadequate intrusion detection. In this work, we propose a hybrid solution that combines Blockchain and artificial intelligence (AI) to improve security and operational efficiency in IoT networks. Blockchain ensures device authentication and data integrity through a lightweight consensus protocol, while AI enables real-time intrusion detection using deep learning models. The simulations demonstrate that the proposed system improves the precision of detecting phishing attacks by up to 95.2%. At the same time, the authentication latency is reduced to 15 ms in networks with 1000 connected devices, 66.6% faster than traditional solutions. In addition, the energy consumption of the hybrid system is 31.8% lower than that of conventional approaches, validating its scalability and efficiency in large-scale IoT networks.

The exponential growth of the Internet of Things (IoT) has boosted connectivity across various sectors, such as Industry 4.0 and smart cities. However, this expansion has also exposed IoT devices to critical vulnerabilities, including spoofing, DoS attacks, and unauthorized access. Traditional security solutions, based on centralized architectures, are neither scalable nor efficient enough to handle the increasing complexity and number of IoT devices, leading to high latencies, increased energy consumption, and inadequate intrusion detection. In this work, we propose a hybrid solution that combines Blockchain and artificial intelligence (AI) to improve security and operational efficiency in IoT networks. Blockchain ensures device authentication and data integrity through a lightweight consensus protocol, while AI enables real-time intrusion detection using deep learning models. The simulations demonstrate that the proposed system improves the precision of detecting phishing attacks by up to 95.2%. At the same time, the authentication latency is reduced to 15 ms in networks with 1000 connected devices, 66.6% faster than traditional solutions. In addition, the energy consumption of the hybrid system is 31.8% lower than that of conventional approaches, validating its scalability and efficiency in large-scale IoT networks.