Pedro Arnau

This research paper presents a methodology consisting of an algorithm and a workflow for finding abandoned dogs in natural surroundings. We propose a temporal-contextual methodology for identifying abandoned pets in public areas. This involves employing a temporal rule alongside context-sensitive object identification, whereby dog bounding boxes are deliberately expanded to encompass proximate visual indicators suggestive of abandonment. The proposed approach uses object detection techniques, trajectory analysis, and image segmentation to quickly differentiate between abandoned and owned dogs. The research addresses key challenges, such as data scarcity and the complexity of distinguishing between abandoned and accompanied dogs. To address the issue of sufficient and adequate training corpus to identify an abandoned dog, the research article employs single-channel image augmentation methods that improve model recall and precision by 4%. Several object detection algorithms were evaluated, and our findings indicate that single-stage detectors like YOLOv8 achieved a better trade-off between classification performance and speed for detecting abandoned dogs compared to multi-stage detectors like Faster R-CNN, reaching a mean average precision up to 86% and an inference time of 0.3 ms per frame. This research study contributes to animal welfare, biodiversity conservation, and public safety by providing a scalable solution for monitoring abandoned animals in diverse environments. The findings demonstrate the impact of object detection techniques on improving the generalization of deep learning models for real-world applications.

We investigate and observe the behavior and performance of Large Language Model (LLM)‐backed chatbots in addressing misinformed prompts and questions with demographic information within the domains of Climate Change and Mental Health. Through a combination of quantitative and qualitative methods, we assess the chatbots' ability to discern the veracity of statements, their adherence to facts, and the presence of bias or misinformation in their responses. Our quantitative analysis using True/False questions reveals that these chatbots can be relied on to give the right answers to these close‐ended questions. However, the qualitative insights, gathered from domain experts, shows that there are still concerns regarding privacy, ethical implications, and the necessity for chatbots to direct users to professional services. We conclude that while these chatbots hold significant promise, their deployment in sensitive areas necessitates careful consideration, ethical oversight, and rigorous refinement to ensure they serve as a beneficial augmentation to human expertise rather than an autonomous solution. Dataset and assessment information can be found at https://github.com/tolusophy/Edge‐of‐Tomorrow.

We investigate and observe the behaviour and performance of Large Language Model (LLM)-backed chatbots in addressing misinformed prompts and questions with demographic information within the domains of Climate Change and Mental Health. Through a combination of quantitative and qualitative methods, we assess the chatbots’ ability to discern the veracity of statements, their adherence to facts, and the presence of bias or misinformation in their responses. Our quantitative analysis using True/False questions reveals that these chatbots can be relied on to give the right answers to these close-ended questions. However, the qualitative insights, gathered from domain experts, shows that there are still concerns regarding privacy, ethical implications, and the necessity for chatbots to direct users to professional services. We conclude that while these chatbots hold significant promise, their deployment in sensitive areas necessitates careful consideration, ethical oversight, and rigorous refinement to ensure they serve as a beneficial augmentation to human expertise rather than an autonomous solution.

Livestock manure has a high ammonium content that can limit its direct application on soil as a fertiliser in nitrate-vulnerable zones. Treatment technologies that are able to extract ammonium from livestock manure allow it to be concentrated in small volumes, making it cheaper and easier to transport and use as fertiliser in crop areas where there is a deficit of nitrogen. This study proposed using low-temperature vacuum evaporation to treat pig slurry in order to obtain marketable products that can be used as fertilisers and help close the nitrogen cycle. Two different configurations and scales were used. The first was a seven-litre laboratory-scale evaporator complemented with a condenser, a condensate trapper, an acid trap and a vacuum pump operated at −90 kPa vacuum pressure and at three different temperatures: 50.1 ± 0.2 °C, 46.0 ± 0.1 °C and 45.3 ± 1.3 °C. The second, Ammoneva, is an on-farm pilot-scale evaporator (6.4 m³), capable of working in four-hour batches of 1 t of liquid fraction of pig slurry with an operating temperature of 40–45 °C and −80 kPa vacuum pressure. The laboratory-scale evaporator, which features several novel improvements focused on increasing ammonia recovery, showed a higher nitrogen removal efficiency from the liquid fraction of pig slurry than the on-farm pilot plant, achieving 84% at 50.1 °C operation, and recovering most of it in ammonia solution (up to 77% of the initial nitrogen), with 7% of the ammonia not recovered. The Ammoneva pilot plant achieved a treated liquid fraction with 41% of initial nitrogen on average, recovering 15% in the ammonia solution in the acid trap; so, the NH₃ gas absorption step needs to be further optimised. However, due to the simplicity of the Ammoneva pilot plant, which is easily placed inside a 20-foot container, and the complete automation of the process, it is suitable as an on-farm treatment for decentralised pig slurry management. The implementation of the novel design developed at laboratory-scale could help further increase recovery efficiencies at the pilot-plant scale.

Cool steam is an innovative distillation technology based on low-temperature thermal distillation (LTTD), which allows obtaining fresh water from non-safe water sources with substantially low energy consumption. LTTD consists of distilling at low temperatures by lowering the working pressure and making the most of low-grade heat sources (either natural or artificial) to evaporate water and then condensate it at a cooler heat sink. To perform the process, an external heat source is needed that provides the latent heat of evaporation and a temperature gradient to maintain the distillation cycle. Depending on the available temperature gradient, several stages can be implemented, leading to a multi-stage device. The cool steam device can thus be single or multi-stage, being raw water fed to every stage from the top and evaporated in contact with the warmer surface within the said stage. Acting as a heat carrier, the water vapor travels to the cooler surface and condensates in contact with it. The latent heat of condensation is then conducted through the conductive wall to the next stage. Net heat flux is then established from the heat source until the heat sink, allowing distilling water inside every parallel stage.

A field of sediment undulations has been mapped by means of high‐resolution multibeam bathymetry and seismic reflection profiles in the Llobregat River prodelta, off the city of Barcelona, Catalonia, Spain. Similar features had previously been recognized in other prodelta environments and interpreted either as downslope sediment deformation or sedimentary structures induced by bottom currents or hyperpycnal flows. Since the study area is undergoing significant offshore development, proper interpretation of such sediment undulations is needed for a correct risk assessment. The occurrence of the sediment undulations is restricted to the prodelta front on slope gradients between 3 and 0.2°. The undulations have developed at the edge and atop an area of gas bearing sediments within the Late Holocene high‐stand mud wedge. An evaluation is made of the characteristics of the sediment undulations in order to determine the most likely process for the origin of these structures. Among these characteristics are the continuity of the reflections and lack of diffractions in between different undulations, their size distribution (large to small) both from shallow to deep and with depth in section, the asymmetry (decreasing from proximal to distal), the crest to trough vertical distance on the landward side of the undulations (up to 0.5 m), and the lack of features that could indicate a progressive movement such as growth structures and drag folds. These characteristics indicate that the sediment undulations on the Llobregat River prodelta do not result from sediment deformation but rather from the interaction of bottom currents generated by hyperpycnal flows from the Llobregat River with regional sea water circulation. Their identification as sediment waves implies that such features do not pose a major hazard for further offshore development.