A deep learning model adjusting for infant gender, age, height, and weight to determine whether the individual infant suit ultrasound examination of developmental dysplasia of the hip (DDH)

ObjectiveTo examine the correlation between specific indicators and the quality of hip joint ultrasound images in infants and determine whether the individual infant suit ultrasound examination for developmental dysplasia of the hip (DDH).MethodWe retrospectively selected infants aged 0–6 months, undergone ultrasound imaging of the left hip joint between September 2021 and March 2022 at Shenzhen Children’s Hospital. Using the entropy weighting method, weights were assigned to anatomical structures. Moreover, prospective data was collected from infants aged 5–11 months. The left hip joint was imaged, scored and weighted as before. The correlation between the weighted image quality scores and individual indicators were studied, with the last weighted image quality score used as the dependent variable and the individual indicators used as independent variables. A Long-short term memory (LSTM) model was used to fit the data and evaluate its effectiveness. Finally, The randomly selected images were manually measured and compared to measurements made using artificial intelligence (AI).ResultsAccording to the entropy weight method, the weights of each anatomical structure as follows: bony rim point 0.29, lower iliac limb point 0.41, and glenoid labrum 0.30. The final weighted score for ultrasound image quality is calculated by multiplying each score by its respective weight. Infant gender, age, height, and weight were found to be significantly correlated with the final weighted score of image quality (P < 0.05). The LSTM fitting model had a coefficient of determination (R2) of 0.95. The intra-class correlation coefficient (ICC) for the α and β angles between manual measurement and AI measurement was 0.98 and 0.93, respectively.ConclusionThe quality of ultrasound images for infants can be influenced by the individual indicators (gender, age, height, and weight). The LSTM model showed good fitting efficiency and can help clinicians select whether the individual infant suit ultrasound examination of DDH

Subjective–objective methodology on selection of power quality compensation technology in AC–DC network with electrified railways

Electrified railways contain DC devices and cause power quality (PQ) issues such as harmonics, unbalance, and low-power factor, which should be solved by the most effective compensation technology to reduce the cost. In addition, it is common that more than one issue is caused at a supply bus, with either some issue affecting more than the others or all issues equally affecting, depending on different buses. Different PQ compensation technologies are used in the past work for the improvement of PQ at the network bus side. However, few of them have provided theoretical justification on which technologies should be selected for different network buses with different issues. This study investigates a subjective–objective methodology to provide suggestions on which compensation technologies should be selected for network buses, why and how. The importance of different PQ issues will be obtained and shown as overall weighting factors. The overall weighting factors are derived from the subjective weighting factor, obtained from existing industry standards, and the objective weighting factor, obtained from the entropy of information. The methodology will be applied to a real 124-bus AC–DC network with electrified railways. Based on the overall weighting factor obtained, the most effective compensation technology can be determined

A Study on the Relationship between Land Use Change and Water Quality of the Mitidja Watershed in Algeria Based on GIS and RS

Many catchments in northern Algeria, including the coastal Mitidja Basin in the north central part of the country have been negatively affected by the deterioration of water quality in recent years. This study aims to discover the relationship between land use change and its impact on water quality in the coastal Mitidja river basin. Based on the data of land use and water quality in 2000, 2010 and 2017, the relationship between land use change and surface water quality index in the Mitidja Watershed was discussed through GIS and statistical analysis. The results show that the physical and chemical properties of the Mitidja river basin have obvious spatial heterogeneity. The water quality of upstream was better than that of downstream. There was a significant spatial relationship between the eight water quality indicators and three land use types, including urban residential land, agricultural land and vegetation. In most cases, settlements and agricultural land are the dominant factors leading to river pollution, and higher vegetation coverage helps to improve water quality. The regression model revealed that percentage of urban settlement area was a predictor for NH4-N, BOD5, COD, SS, PO4-P, DO and pH, while vegetation was a predictor for NO3-N. The analysis also showed that during this period, urban settlement areas increased sharply, which has a significant impact on water quality variables. Agricultural land only had a significant positive correlation with PO4-P. The results provide an effective way to evaluate river water quality, control water pollution and land use management by landscape pattern