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Soldo [1] raises concerns about the reliability of citizen science-derived photographic records for identifying lamnid sharks in the Mediterranean Sea. Using a recently published juvenile white shark from Croatian waters [2] as a representative case, the author reinterprets the specimen as a porbeagle shark (Lamna nasus) rather than a white shark (Carcharodon carcharias), based primarily on selected dental characters. Given the critically endangered status of white sharks in the Mediterranean [3] and the limited number of confirmed observations [4,5], occurrences of the species in this region serve as valuable population data points [6,7] and merit careful examination. Species identifications and reinterpretations should therefore be well-supported, which includes verification of source materials and firsthand communication with citizen scientists, and make use of multiple diagnostic characters whenever possible. Here, we re-examined the specimen using all observable external and dental characters and show that it is consistent with the white shark. We further demonstrate that reliance on single, variably expressed traits-particularly dental characters affected by ontogeny and individual variation-can lead to erroneous identifications, and emphasize that rigorous taxonomic identifications should depend on a holistic, integrative assessment of all available morphological (and if available genetic) evidence.

When the Asian tiger mosquito (Aedes albopictus) was discovered in the EU for the first time, in a kindergarten in Genua (Italy) in 1990, it was followed initially by a call for action to stop its spread, but gradually turned into a 'study object', resulting in hundreds of research papers since, but very little action in terms of actually trying to eliminate it. Europe is now facing the grave consequences of this lack of action, with dengue, Chikungunya, and West Nile virus already creating problems around the Mediterranean, and aided by climate change both the mosquito and these diseases will move farther north in years to come, posing a risk to millions of people. This history is now repeating itself in the Horn of Africa. It's been thirteen years since Anopheles stephensi, an Asian malaria vector, invaded Djibouti, and has not had much going against it since. And like in Europe, stephensi has become 'the new kid on the block' for academics, and paper after paper (87 in the last 3 years) is coming online, always calling for action in the final paragraphs, but forgetting that mosquitoes don't care about papers and happily continue to conquer more terrain as we sit down to read another paper. Research should focus exclusively on four things: where is the mosquito (surveillance), what works to kill it (control), how do we free areas of it (implementation strategy) and how can we prevent re-invasion of cleared areas. In two of these areas we're almost clueless: strategic area-wide vector elimination and preventing re-invasion. Action in these areas is therefore critical if Africa is to prevent repeating Europe's mistake. And should start with garnering unwavering support and resources from governments of affected countries, regional organisations, and global players. The stephensi problem will not be solved with a dipper searching for larvae in the field. It should start by knocking on politicians' doors, intense lobbying, and succeeding in having the problem addressed with utmost priority. Only then will the means and resources become available to attempt elimination of this invasive vector. Critically, these resources should not affect any ongoing malaria control efforts but should be freed specifically for this purpose. On a more positive note, going aggressively after stephensi would concurrently teach us ways to go after indigenous African vectors in an area-wide fashion, notably through larval source management. Inertia will merely push us all decades backwards in terms of hoping to eliminate malaria in Africa one day.

All mammals, including marine mammals, lose hearing sensitivity with age (presbycusis), particularly at higher frequencies. Killer whales (Orcinus orca) are one of the most cosmopolitan marine mammal species, and they experience exposure to many anthropogenic noise sources, which makes it important to understand how individuals may be affected by age-related hearing loss. Previous audiograms have been published on this species using both physiological and behavioral methods, but the effects of presbycusis on this species have never been studied. For this study, audiograms from multiple killer whales housed at SeaWorld San Diego were repeated for direct, within-subjects comparison to a study conducted nearly a decade prior (Branstetter et al., 2017). Subjects ranged from 12 to 60 years of age. The results provided evidence of presbycusis in killer whales, which is consistent with other studies in marine mammals. Monitoring these changes is important for ensuring optimal welfare of the whales, providing a better representation for population-level hearing, and benefiting the conservation of wild killer whales.

The increasing complexity of X-ray test data and the demand for precise, large-scale evaluations have exposed the limitations of manual flaw detection methods in Non-Destructive Testing (NDT). Traditional manual approaches, while widely used, are labor-intensive and prone to human error, often leading to inconsistencies and inefficiencies. An Artificial Intelligent driven (AI-driven) system has been developed to automate the analysis and evaluation of X-ray test data, improving both accuracy and efficiency. The system employs advanced image segmentation and classification algorithms, aligning with ISO standards to detect defects in welded structures. By reducing reliance on manual interpretation, the system enhances the reliability and speed of the evaluation process, while also allowing for expert oversight through manual corrections when necessary. Integration with a Laboratory Information Management System (LIMS) ensures streamlined data handling and traceability, minimizing human error in data recording. As the AI model processes more data, it continuously improves, adapting to evolving defect patterns and maintaining high performance. This paper details the system’s AI architecture, the methodology employed for X-ray image analysis, and the performance results from industrial applications, demonstrating how this technology addresses key challenges in NDT processes.