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Advanced Engineering Science

Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-29-07-2023-601

Abstract : Fresh cheese holds a special place in Ecuadorian households, characterized by unique sensory attributes influenced by local culture and geography. Due to its perishable nature and traditional artisanal production process, adherence to Good Manufacturing Practices (GMP) is crucial for ensuring the quality and safety of the final product. This study aimed to assess the compliance with minimum hygiene requirements in fresh cheese production establishments. The evaluation took place at the beginning and end of a two-year community engagement project. Using a tailored checklist based on relevant regulatory aspects, establishments in the cantons of Zaruma and Piñas, major dairy product hubs in El Oro province, were evaluated. The majority of the establishments improved their physical infrastructure and streamlined production processes, addressing identified weaknesses. These enhancements are expected to elevate the quality and safety of the cheese, ensuring consumer food safety. The research established that reinforcing manufacturing practices and upholding GMP standards directly and positively impact the quality and safety of the fresh unaged cheese, an emblematic culinary delight in Ecuador. This research emphasizes the need to maintain a proactive approach and support producers in crafting this cherished traditional dairy product, preserving the trust of Ecuadorian families in savoring this delectable cheese at their tables. Fresh cheese in Ecuador boasts distinct sensory characteristics shaped by local influences and the adherence to GMP is vital due to its perishable nature and artisanal production. The study assessed hygiene compliance in cheese production establishments, leading to significant improvements. The research highlights the direct link between GMP and cheese quality and safety, stressing the importance of continued support to maintain excellence in producing this treasured dairy product..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-25-07-2023-600

Abstract : Drought is a recurring natural disaster that can cause significant damage to agricultural production, human livelihoods, and the environment. Drought forecasting is an important tool for managing and mitigating the impacts of drought. This study aimed to improve drought forecasting through the use of machine learning models. Specifically, the study evaluated the performance of three machine learning models, namely Extreme Learning Machine (ELM), Random Forest (RF), and Support Vector Regression (SVR), for forecasting Standardized Precipitation Index (SPI) drought. These models were trained using precipitation data of Hiran region, Somalia from 1980 to 2021, to evaluate their ability to accurately predict drought conditions. The results showed that the SVR model performed the best, with an R2 value of 0.753, MAE of 0.344, and RMSE of 0.488. The ELM and RF models also performed well. The study highlights the potential of machine learning models to improve drought forecasting, and the importance of evaluating multiple models to select the one that performs best for a specific dataset..
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