Background Cervix Squamous cell carcinoma(CSCC) is the most common pathological subtypes of cervix carcinoma(CC). CSCC can be divided into poorly differentiated, moderately differentiated and well-differentiated types. The pathological differentiation is essential for the treatment and prognosis of CSCC. Compared with the well-differentiated CSCC patients, poorly differentiated CSCC patients have poor clinical prognosis. The biopsy is the golden standard for identifying pathological differentiation with the disadvantages including invasive. Therefore, an imaging method is needed to determine the degree of tumor differentiation before surgery. Purpose The objective is to explore APTw and IVIM values in diagnosing the differentiation degree of cervical squamous cell carcinoma (CSCC). Methods APTw was scanned by using 3D Multi-shot TSE for obtaining APT signal intensity (APT SI). IVIM was scanned by using 12 b values (0, 20, 100, 150, 200, 300, 400, 500, 600, 800, 1000 and 1200 s/mm2) to calculate parameters: D, D*, and f. ADC was calculated based on 2 b values (0, 800 s/mm2). The parameters among different groups were compared by t-tests. Diagnostic performance was evaluated with a ROC analysis. Results 56 patients and 30 healthy volunteers were included in study. Patients were divided into: a well-moderately differentiated group (n = 34) and a poorly differentiated group (n = 22). The parameters (APT SI, ADC, D, f) were statistically significantly different between CSCC and normal cervix. APT SI of the CSCC was higher than that of normal cervix (P < 0.001). The ADC, D, and f of the CSCC were lower than those of normal cervix (P < 0.001). Significant differences were found in APT SI and D between the well-moderately differentiated and poorly differentiated group (P < 0.001). Comparing the well-moderately differentiated and poorly differentiated group, AUC of APT SI, D and f were 0.789, 0.775 ,and 0.670, sensitivity were 72.73%, 68.18%, 77.27%, and specificity were 79.41%, 82.35%, 64.71%, respectively (P < 0.05). Conclusion APTw and IVIM can be used to diagnose CSCC and provide accurate quantitative information. Compared with IVIM, APTw has higher diagnostic performance in identifying the differentiation degree of CSCC.

Aiming at the problems of poor layout design efficiency of smart home products and low rationality of layout planning, a layout design method for smart home products based on a real-number coding genetic algorithm is proposed. The principles of smart home product layout design are analyzed and the smart home system architecture based on the Internet of Things is designed. Divide the combination of smart home product layout space according to spatial function, extract the visual features of smart home product layout, build a smart home product layout optimization model based on the two constraints of the total area and individual area of smart home product spatial layout, and design a real-coded genetic algorithm. The model is solved to improve the global convergence of the algorithm, and the optimization method for the layout of smart home products is obtained. The experimental results show that the layout design method of smart home products based on a real-number coding genetic algorithm can accurately extract the layout features of smart home products and accurately classify the number of pairs of homes. The efficiency of layout design and rationality of layout planning of smart home products are better, which reflects the effectiveness of this method.