Parkinson's disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in the basal ganglia, impacting millions of individuals globally. The clinical manifestations of the disease include resting tremors, muscle rigidity, bradykinesia, and postural instability. Diagnosis relies mainly on clinical evaluation, lacking reliable diagnostic tests and being inherently imprecise and subjective. Early detection of PD is crucial for initiating treatments that, while unable to cure the chronic condition, can enhance the life quality of patients and alleviate symptoms. This study explores the potential of utilizing long-short term memory neural networks (LSTM) with attention mechanisms to detect Parkinson's disease based on dual-task walking test data. Given that the performance of networks is significantly inductance by architecture and training parameter choices, a modified version of the recently introduced crayfish optimization algorithm (COA) is proposed, specifically tailored to the requirements of this investigation. The proposed optimizer is assessed on a publicly accessible real-world clinical gait in Parkinson's disease dataset, and the results demonstrate its promise, achieving an accuracy of 87.4187 % for the best-constructed models.
Seagrasses have the ability to contribute towards climate change mitigation, through large organic carbon (Corg) sinks within their ecosystems. Although the importance of blue carbon within these ecosystems has been addressed in some countries of Southeast Asia, the regional and national inventories with the application of nature-based solutions are lacking. In this study, we aim to estimate national coastal blue carbon stocks in the seagrass ecosystems in the countries of Southeast Asia including the Andaman and Nicobar Islands of India. This study further assesses the potential of conservation and restoration practices and highlights the seagrass meadows as nature-based solution for climate change mitigation. The average value of the total carbon storage within seagrass meadows of this region is 121.95 ± 76.11 Mg ha-1 (average ± SD) and the total Corg stock of the seagrass meadows of this region was 429.11 ± 111.88 Tg, with the highest Corg stock in the Philippines (78%). The seagrass meadows of this region have the capacity to accumulate 5.85-6.80 Tg C year-1, which accounts for $214.6-249.4 million USD. Under the current rate of decline of 2.82%, the seagrass meadows are emitting 1.65-2.08 Tg of CO2 year-1 and the economic value of these losses accounts for $21.42-24.96 million USD. The potential of the seagrass meadows to the offset current CO2 emissions varies across the region, with the highest contribution to offset is in the seagrass meadows of the Philippines (11.71%). Current national policies and commitments of nationally determined contributions do not include blue carbon ecosystems as climate mitigation measures, even though these ecosystems can contribute up to 7.03% of the countries' reduction goal of CO2 emissions by 2030. The results of this study highlight and promote the potential of the southeast Asian seagrass meadows to national and international agencies as a practical scheme for nature-based solutions for climate change mitigation.