Modeling runoff-sediment influx responses to alternative BMP interventions in the Gojeb watershed, Ethiopia, using the SWAT hydrological model.

The Soil & Water Assessment Tool (SWAT) has been calibrated over a 33-year period to evaluate the Gojeb watershed's hydrological processes, sediment yield with downstream loading to the Gibe III dam, and erosion hotspot locations. Best management practices (BMPs) were run through the model to simulate the effects of watershed intervention scenarios on sediment yield and runoff. Simulation results of BMP intervention were compared with the reference and worst-case scenarios. The simulation of sediment production indicates a clear growing trend. Temporally, the maximum amount of sediment transported out of the watershed is experiential from June to September, and the minimum is in February. A plainly defined similar orientation is observed between precipitation, surface runoff, and sediment load in the landscape. Spatially, the maximum sediment transported out of the watershed is from agricultural landscape units with a slope of over 50%, annual precipitation above 1592 mm, and surface runoff over 151 mm. This signifies that the watershed is under serious threat from erosion due to vegetation loss, steep slope farming, and high surface runoff. Gibe III is a 243-m high roller compacted gravity dam built on the Omo-Gibe River basin in Ethiopia for hydroelectric power and downstream flood control. It is one of Africa's tallest dams, with an annual electric output of 1870 MW that began operation in 2016. Thus, Gibe III could see a loss of storage capacity due to higher-than-expected sedimentation resulting from worsening environmental degradation, which implies that the beneficial uses that depend on this dam - electricity production, regulated irrigation water supply, and flood control - will decline with significant economic losses. Despite that, selected sustainable land management interventions and the application of BMPs to critical erosion-prone hotspot areas can support the overall reduction in total sediment yield and surface runoff.

Investigation of plastic waste management in Thailand using material flow analysis.

This study uses material flow analysis to investigate the amounts and pathways of municipal plastic waste (MPW) in Thailand. The aim is to understand the country's situation and investigate the effects of Thailand's Roadmap on Plastic Waste Management 2018-2030, which sets a goal for recycling 'target plastic waste' at 100% by 2027. The analysis was conducted using waste statistics between 2008 and 2020 and waste forecasts. Two scenarios of plastic waste management, the business-as-usual, and the national roadmap, were constructed for 2025 and 2030. In 2030, the annual MPW generation is projected to reach 2.19 Mt. Under the business-as-usual (BAU) scenario, the recycling and utilisation rate will be 32.3% of waste generated. About 30.3% of waste generated will not be treated properly and possibly leaked into the open environment. Under the roadmap scenario, the recycling and utilisation rate will increase to 98.4%, while 1.6% of waste generated will not be treated properly. The recycling rate for target plastic waste in 2027 could only reach 67.1% because plastic waste is required as fuel for waste incinerators and industries. With the roadmap fully implemented, certain effects can be foreseen for waste-to-energy and plastic industries. Findings from this study stress on the importance of holistic policy planning, efficient prioritising and allocating of waste as a resource, and cooperation from all sectors for sustainable plastic waste management.

A Study on the Impacts of Slot Types and Training Data on Joint Natural Language Understanding in a Spanish Medication Management Assistant Scenario.

This study evaluates the impacts of slot tagging and training data length on joint natural language understanding (NLU) models for medication management scenarios using chatbots in Spanish. In this study, we define the intents (purposes of the sentences) for medication management scenarios and two types of slot tags. For training the model, we generated four datasets, combining long/short sentences with long/short slots, while for testing, we collect the data from real interactions of users with a chatbot. For the comparative analysis, we chose six joint NLU models (SlotRefine, stack-propagation framework, SF-ID network, capsule-NLU, slot-gated modeling, and a joint SLU-LM model) from the literature. The results show that the best performance (with a sentence-level semantic accuracy of 68.6%, an F1-score of 76.4% for slot filling, and an accuracy of 79.3% for intent detection) is achieved using short sentences and short slots. Our results suggest that joint NLU models trained with short slots yield better results than those trained with long slots for the slot filling task. The results also indicate that short slots could be a better choice for the dialog system because of their simplicity. Importantly, the work demonstrates that the performance of the joint NLU models can be improved by selecting the correct slot configuration according to the usage scenario.

Principles of Cancer Staging for Clinical Obstetrics and Gynecology.

Cancer represents a complex group of diseases characterized by uncontrolled growth and the ability to metastasize. Cancer may affect any part of the body, and within the female reproductive systems, there exist a variety of cancers each associated with different presenting symptoms, clinical course, etiology, and natural history of disease. The essential features of each cancer include the presenting site of disease (topography), the histopathologic (morphology), molecular and genetic tumor profile, and the anatomic disease extent (stage). Without knowing these features, it is impossible to discuss investigation, treatment, and prognosis in cancer.