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The growing concern on global warming has pushed to set ambitious targets of carbon neutrality or net zero at the water sector. Meanwhile, poor data availability has been reported to restrict the national assessment of climate impacts and mitigation strategies in water sector. In national greenhouse gas (GHG) inventories, water sector is embedded in other sectors' emissions making it difficult to monitor separately. This study presents a national scale evaluation of climate change impacts for water sector in Finland based on life cycle analysis (LCA). In addition, the effectiveness of currently available emission reduction measures is evaluated by scenario analysis until the year 2035. According to the results, the life cycle climate change impacts from the Finnish municipal water sector were 0,67 (0,46-0,88) million tonnes CO2-eq./year (142.8 (98.9-187.1) kg CO2-eq./person/year). Drinking water services accounted for 12.5-13.9 % and wastewater services 86.1-87.4 % of the total emissions. With currently feasible emission reduction measures, the climate change impacts could be reduced approximately 14-30 % in total by 2035. The aim of carbon neutrality in the water sector was found to be unrealistic to achieve with existing and currently feasible measures for Finland and thus significant new emission mitigation measures are needed. The vague definition of carbon neutrality and system boundary of water sector as well as the uncertainties related to the assessment of direct emissions, undermine the credibility of the ambitiously set target. Prioritizing emission offsets to reach the target may inadvertently lead to unintended negative consequences due to the limitations and incompleteness of offset methods.

ABSTRACTVariation in climatic conditions is expected to impact the distribution and abundance of herbivore species, which may, in turn, influence African lions (Panthera leo) ranging behaviour. This necessitates proactive management of lion populations, as ecosystems may exhibit site‐specific responses to these variations. Using satellite and GPS‐GSM data from 10 lions collared in three different national parks in Kenya—Meru, Nairobi, and Lake Nakuru—we calculated monthly home ranges and distance moved and studied how these were influenced by rainfall. Across all parks, lions increased their distance moved with an increase in rainfall, and male lions covered larger distances than females. Model comparisons revealed that lions in Lake Nakuru significantly expanded their home ranges with higher rainfall compared to those in Nairobi; however, lions in Nairobi covered larger distances than those in Lake Nakuru. Lions in Meru had larger home ranges compared to the other two parks, but the effect of rainfall on their home range size and movement was not significantly different from the other two parks. Our results give insight into the site‐specific influence of rainfall on lion home range and movement across the parks. We call for prioritisation of conservation efforts and a site‐specific, tailored approach to lion conservation and management.

Organizations are struggling to ensure knowledge workers’ productivity and innovative capabilities to adopt rapidly changing technologies. This study investigates this under-explored topic of impediments for knowledge workers’ sustainable productivity and innovative capabilities for digitalization and adoption of Industry 4.0 and 5.0 using multi-level and multi-dimensional research approach. Using in-depth structured literature review and refined through expert evaluation, a list of impediments was developed which was further categorized at multiple levels along with cross country comparative analyses using survey data collected from IT professionals from six countries. Analytical Hierarchal Process approach is used to prioritize impediments at four levels of impediment ranking, factors ranking, categories ranking and global ranking along with country-wise analysis to explore IT professionals’ preference. Findings suggest focus areas in three dimensions - organizational, technological, and personal along with three factors of each using relative rankings based on local and global weights. The study has significant theoretical and practical implications in relation to digitalization, knowledge workers and adoption of industry 4.0 and 5.0. Post-print / Final draft

Abstract The hoarding behaviour of animals has evolved to reduce starvation risk when food resources are scarce, but effects of food limitation on survival of hoarding animals is poorly understood. Eurasian pygmy owls (Glaucidium passerinum) hoard small mammals and birds in natural cavities and nest boxes in late autumn for later use in the following winter. We studied the relative influence of the food biomass in hoards of pygmy owls on their over‐winter and over‐summer apparent survival. We also tested whether this influence is modulated by intrinsic (age, sex) traits or extrinsic factors (winter temperature, snow depth). We measured biomass of prey items in pygmy owl food‐hoards during autumns 2003–2023 in west‐central Finland. We individually marked and recaptured pygmy owls both at nests in the breeding season and at food‐hoards. Our dataset included a total of 407 pygmy owls, which were all captured from a food‐hoard at least once during their capture history. The mean biomass of the annual food‐hoards associated with one individual was 443 g (SD = 523 g, range from 3.5 to 4505 g) and was markedly higher in autumns of vole abundance than in those of vole scarcity. Hoard size had a positive effect on apparent survival of owls over consecutive winter, whereas it did not affect apparent survival over next summer. Hoard size was a better predictor of apparent survival than vole abundance (main food of pygmy owls) in the field. Male owls had higher overall apparent survival rates than female owls, particularly when food‐hoards were small. That hoard size was a better predictor of apparent survival than vole abundance indicates that the hoards are critical for pygmy owls during winter, likely because they are unable to hunt voles below deep snow cover. The positive relationship between apparent survival of owl individuals and their hoard size during winter (when the hoard is being consumed), but not summer, indicates that the hoard size has a true positive effect on survival, and does not only reflect latent inter‐individual differences and/or dissimilarities in their environments. We conclude that food limitation during hoarding essentially regulates apparent over‐winter survival of pygmy owl individuals.

In recent years, the integration of renewable energy sources in power systems has been increasing. Their inherent unpredictability and output fluctuations pose challenges to secure power system operations and energy market pricing stability. Therefore, an accurate forecast of renewable energy generation is crucial. Several effective forecasting methods that have been applied are based on Machine Learning (ML). A key factor in the application of ML methods is the choice of input features, a task that has become more complex in regional wind power forecasting, where regions can cover entire countries. The proposed method aims to improve forecasting performance by streamlining input features through a data-driven model-agnostic preprocessing technique. This involves splitting the multidimensional numerical weather predictions into subareas and eliminating non-informative subareas. The selection of optimal split and remove parameters is guided by a Bayesian sequential optimisation process, which builds on prior knowledge from previous iterations. The proposed method has been implemented on actual wind power measurements aggregated at regional level for three countries located in Southeastern Europe to demonstrate the effectiveness in improving the performance of popular data-driven forecasting methods. ; ©2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ; fi=vertaisarvioitu|en=peerReviewed|

A thermal‐energy‐storage (TES) system is investigated in this work. The charging process uses hot air passed through a fixed bed, transferring thermal energy to solid particles, while discharging occurs with cold air flowing in the opposite direction. A novel automated dynamic simulation model of the TES is developed and validated using data from the literature. This study uniquely operates with a heat‐transfer‐fluid (HTF) temperature of up to 1200 °C during charging, with discharge temperatures regulated via a bypass controller. Simulations explore the fixed‐bed storage behaviour during charging/discharging cycles, with 64 parameter variations tested. In addition to air, CO2 is evaluated as an HTF to enhance performance due to its higher density. Results show that Case C14 (using air) achieves a maximum thermal capacity of 3.237 MWh and utilization of 55.4%. When CO2 is substituted for air under the same parameters, a thermal capacity and utilization increase of 4.5% is observed, along with reduced compressor work, highlighting CO2's advantages for improved efficiency.

The integration of Unmanned Aerial Vehicles (UAVs) and Low Earth Orbit (LEO) satellites has become attractive for Internet of Things (IoT) task processing, as it can overcome obstacles in terrestrial network coverage, such as those in oceans or desert areas. However, it lacks a collaborative approach for allocating the communication and computing resources among UAVs and LEO satellites and optimizing the hovering point of UAVs to prolong their endurance. In this paper, we investigate energy-efficient resource allocation in LEO-assisted UAV networks for the Internet of Things. A novel optimization algorithm, that Jointly IoT tasks' Offloading decision, UAVs' Region selection, Hovering point chosen, and Communication and Computing resource allocation (ORHCC), is proposed to optimize UAV trajectories and hovering points, enhancing endurance and minimizing energy consumption. In particular, the UAVs' region selection and IoT tasks offloading are under the Dueling Deep Q-Network (DuDQN) framework, the Hovering point chosen and Communication and Computing resource allocation via the convex solution. The results show that the proposed ORHCC reduces 12.5% and 20.76% energy consumption compared with the PPO and greedy baseline, respectively.