The suspicion of mediastinal alterations, always includes in its initial study, the chest radiography. The identification of mediastinal alterations in the X-ray is a priority. The knowledge of the mediastinal references and the identification of their alterations allows the suspicion of a pathology specific to each of the mediastinal spaces. When the semiology of mediastinal lesions, their location and the three most frequent pathologies are taken into account, the possibility of having an etiological diagnosis increases^[1]. This is a review article based on a detailed literature search, in which radiological mediastinal references are studied, with emphasis on the epidemiological data of each one of them.

This paper mainly uses the idea of pedigree clustering analysis, gray prediction and principal component analysis. The clustering analysis model, GM (1,1) model and principal component analysis model were established by using SPSS software to analyze the correlation matrices and principal component analysis. MATLAB software was used to calculate the correlation matrices. In January, The difference in price changes of major food prices in cities is calculated, and had forecasted the various food prices in June 2016. For the first issue, the main food is classified and the data are processed. After that, the SPSS software is used to classify the 27 kinds of food into four categories by using the pedigree cluster analysis model and the system clustering. The four categories are made by EXCEL. The price of food changes over time with a line chart that analyzes the characteristics of food price volatility. For the second issue, the gray prediction model is established based on the food classification of each kind of food price. First, the original data is cumulated, test and processed, so that the data have a strong regularity, and then establish a gray differential equation, and then use MATLAB software to solve the model. And then the residual test and post-check test, have C <0.35, the prediction accuracy is better. Finally, predict the price trend in June 2016 through the function. For the third issue, we analyzed the main components of 27 kinds of food types by celery, octopus, chicken (white striped chicken), duck and Chinese cabbage by using the data of principal given and analyzed by principal component analysis. It can be detected by measuring a small amount of food, this predict CPI value relatively accurate. Through the study of the characteristics of the region, select Shanghai and Shenyang, by looking for the relevant CPI and food price data, using spss software, principal component analysis, the impact of the CPI on several types of food, and then calculated by matlab algorithm weight, and then the data obtained by the analysis and comparison, different regions should be selected for different types of food for testing.

This research focused on the design and implementation of the flipped classroom approach for higher mathematics courses in medical colleges. Out of 120 students, 60 were assigned to the experimental group and 60 to the control group. In the continuous assessment, which included homework and quizzes, the average score of the experimental group was 85.5 ± 5.5, while that of the control group was 75.2 ± 8.1 (P < 0.05). For the final examination, the average score in the experimental group was 88.3 ± 6.2, compared to 78.1 ± 7.3 in the control group (P < 0.01). The participation rate of students in the experimental group was 80.5%, significantly higher than the 50.3% in the control group (P < 0.001). Regarding autonomous learning ability, the experimental group spent an average of 3.2 hours per week on self-study, compared to 1.5 hours in the control group (P < 0.005). Other potential evaluation indicators could involve the percentage of students achieving high scores (90% or above) in problem-solving tasks (25.8% in the experimental group vs. 10.3% in the control group, P < 0.05), and the improvement in retention of key concepts after one month (70.2% in the experimental group vs. 40.5% in the control group, P < 0.01). In conclusion, the flipped classroom approach holds substantial promise in elevating the learning efficacy of higher mathematics courses within medical colleges, offering valuable insights for educational innovation and improvement.

The Organic Rankine Cycle (ORC) is an electricity generation system that uses organic fluid instead of water in the low temperature range. The Organic Rankine cycle using zeotropic working fluids has wide application potential. In this study, data mining (DM) model is used for performance analysis of organic Rankine cycle (ORC) using zeotropik working fluids R417A and R422D. Various DM models, including Linear Regression (LR), Multi-Layer Perceptron (MLP), M5 Rules, M5 Model Tree, Random Committee (RC), and Decision Tree (DT) models are used. The MLP model emerged as the most effective approach for predicting the thermal efficiency of both R417A and R422D. The MLP’s predicted results closely matched the actual results obtained from the thermodynamic model using Genetron software. The Root Mean Square Error (RMSE) for the thermal efficiency was exceptionally low, at 0.0002 for R417A and 0.0003 for R422D. Additionally, the R-squared (R²) values for thermal efficiency were very high, reaching 0.9999 for R417A and R422D. The findings demonstrate the effectiveness of the DM model for complex tasks like estimating ORC thermal efficiency. This approach empowers engineers with the ability to predict thermal efficiency in organic Rankine systems with high accuracy, speed, and ease.

Brunei Darussalam is a small Sultanate country with diverse forest cover. One of them would be Mangrove Forest. As it has four main administrative districts, Temburong would be the chosen case study area. The methods of collecting data for this article are by collecting secondary data from official websites and the map in this article (Figure 1) are showing the forest cover in Brunei Darussalam as of 2020. The aim of this article is to explain the mangrove forest especially at the Temburong District. As for the objectives, it would to be able to show the different types of forests in Temburong, hoping in ability to explain the different subtypes of mangroves forest and to explain in general the green jewel of Brunei Darussalam. Temburong has become the second highest tree coverage in Brunei Darussalam of 124 kha as of 2010, while the mangrove forest covering about 66% of total mangrove forest of 12,164 km² out of 18,418 hectares. Mangrove forest has seven subtypes: Bakau species, Nyireh bunga, Linggadai, Nipah, Nipah-Dungun, Pedada and Nibong. Selirong Forest Reserve and Labu Forest Reserve are the two-mangrove forest reserves in Brunei Darussalam at Temburong District. Forest cover in Brunei Darussalam are 3800 hectares as of 2020 and has lost its tree coverage of 1.17 kha and one of the reasons would be forest fire and the tree cover loss due to fire is around 197 ha and the district that has lost its tree cover mostly was at Belait District of total 13.4 kha between the year 2001 until 2022.