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Background: Postoperative admission to the surgical intensive care unit (S-ICU) is commonly planned to prevent and treat complications, unnecessary admission to the S-ICU increases medical costs and length of hospital stay. This study aimed evaluated outcome and the predictive factors for mortality in patients admitted to the S-ICU after abdominal surgery. Methods: The 168 patients admitted to the S-ICU immediately after abdominal surgery were reviewed retrospectively from January to December 2011. Results: The mortality rate of patients admitted to the S-ICU after abdominal surgery was 8.9% (15 of 168). Two preoperative factors (body mass index [BMI] < 18.5 kg/m2 [p < 0.001] and serum albumin < 3.0 g/dL [p = 0.018]), two operative factors (the need for transfusion [p = 0.008] or vasopressors [p = 0.013] during surgery), and three postoperative variables (mechanical ventilation immediately following surgery [p < 0.001], sequential organ failure assessment [p = 0.001] and SAPS II [p = 0.001] score) were associated with mortality in univariate analysis. After adjusting for age, gender, and SAPS II by a Cox regression, which revealed that BMI < 18.5 kg/m2 (p < 0.001, hazard ratio [HR] 9.690, 95% confidence interval [CI] 2.990-25.258) and the use of mechanical ventilation on admission to S-ICU (p < 0.001, HR 34.671, 95% CI 6.440-186.649) were independent prognostic factors. Conclusions: In patients in S-ICU after abdominal surgery, low BMI and postsurgical mechanical ventilation should be considered important predictors of mortality.

Various data types generated in the semiconductor manufacturing process can be used to increase product yield and reduce manufacturing costs. On the other hand, the data generated during the process are collected from various sensors, resulting in diverse units and an imbalanced dataset with a bias towards the majority class. This study evaluated analysis and preprocessing methods for predicting good and defective products using machine learning to increase yield and reduce costs in semiconductor manufacturing processes. The SECOM dataset is used to achieve this, and preprocessing steps are performed, such as missing value handling, dimensionality reduction, resampling to address class imbalances, and scaling. Finally, six machine learning models were evaluated and compared using the geometric mean (GM) and other metrics to assess the combinations of preprocessing methods on imbalanced data. Unlike previous studies, this research proposes methods to reduce the number of features used in machine learning to shorten the training and prediction times. Furthermore, this study prevents data leakage during preprocessing by separating the training and test datasets before analysis and preprocessing. The results showed that applying oversampling methods, excluding KM SMOTE, achieves a more balanced class classification. The combination of SVM, ADASYN, and MaxAbs scaling showed the best performance with an accuracy and GM of 85.14% and 72.95%, respectively, outperforming all other combinations. [ABSTRACT FROM AUTHOR]

Studying social impacts and responses to historical extreme climate events can offer valuable insights into coping with major disaster events and adapting to climate change better. This paper developed a model of the processes and responses to extreme drought-induced famines in ancient China. Based on this, the study explored the differences in famine causation and response effectiveness between Chenghua Drought (1483–1486 CE) and Wanli Drought (1585–1588 CE). The findings are as follows: (1) By the time of Wanli Drought, the increase in land reclamation and introduction of American crops had enhanced societal defence, preventing many drought-affected counties from experiencing famine. Even in cases where famines did occur, their severity was lower than during the Chenghua Drought. (2) State emergency measures, including exemption and relief, proved effective in mitigating famines. The stronger finance and economy during the Wanli Drought enabled more robust relief efforts, resulting in lower famine severity. (3) Famine response capabilities varied regionally. Shandong, Beijing, Tianjin and Hebei demonstrated strong defensive capabilities and effective state emergency responses, while Shanxi had weaker defensive abilities, making it more vulnerable to famine. The defensive capabilities in Henan, Shaanxi, Gansu, and Ningxia showed significant improvement. [ABSTRACT FROM AUTHOR]

Salt stress is one of the environmental stresses that significantly reduces crop productivity and quality worldwide. Methods to overcome salt stress include developing salt-resistant crops by inserting various resistance genes or to diagnosing and responding to the effects of salt stress at an early stage. In this study, we investigate the effects of salinity stress on growth, photosynthetic efficiency, and metabolic changes in Brussels sprouts (Brassica oleracea var. gemmifera). Fresh weight and leaf area decreased significantly with increasing NaCl concentration, indicating that salinity stress has a detrimental effect on plant growth. However, chlorophyll fluorescence parameters did not show significant changes, suggesting that photosynthetic efficiency was not significantly affected over 10 days. Fourier transform infrared (FTIR) spectroscopy revealed notable metabolic adjustments, especially in lipids, plastids, proteins, and carbohydrates, indicating biosynthesis of protective compounds such as anthocyanins and proline in response to salinity stress. Pearson correlation analysis confirmed a strong relationship between NaCl concentration and the observed physiological and metabolic changes. The findings highlight the potential of FTIR spectroscopy as a non-destructive tool for early detection of salinity stress and timely intervention to improve crop resilience and yield. This study highlights the widespread application of FTIR spectroscopy in agricultural research to manage abiotic stresses in crops. [ABSTRACT FROM AUTHOR]