Development and Application of Multi Commodity Multi Location Integrated Model for Manufacturing Industry

This paper presented the development and application of Linear Programming to the modeling of Multi-Commodity Multi-Location production-distribution model for manufacturing industry. The Manufacturing industry has two plants, three depots and twenty retailer’s axis in Lagos. The products are based on how they are packaged; Product 1(P1), Product 2(P2), Product 3(P3) and Product 4(P4). TORA software is used in analyzing the data obtained from the company. Comparing the optimal Multi-Commodity Multi-Location transportation cost of One trillion, Five Hundred And Thirty Billion And Four Hundred And Ninety Million Naira to existing transportation cost of truckload Three Trillion, Five Hundred And Forty Four Billion Naira, the difference is Two Trillion, Thirteen Billion And Five Hundred And Ten Million Naira which is Four Hundred And Two Billion And Seven Hundred And Two Million Naira annually resulting to 56.82 percent gain in profit.

Advanced Warning of Aortic Dissection on Non-Contrast CT: The Combination of Deep Learning and Morphological Characteristics

Background: The identification of aortic dissection (AD) at baseline plays a crucial role in clinical practice. Non-contrast CT scans are widely available, convenient, and easy to perform. However, the detection of AD on non-contrast CT scans by radiologists currently lacks sensitivity and is suboptimal.Methods: A total of 452 patients who underwent aortic CT angiography (CTA) were enrolled retrospectively from two medical centers in China to form the internal cohort (341 patients, 139 patients with AD, 202 patients with non-AD) and the external testing cohort (111 patients, 46 patients with AD, 65 patients with non-AD). The internal cohort was divided into the training cohort (n = 238), validation cohort (n = 35), and internal testing cohort (n = 68). Morphological characteristics were extracted from the aortic segmentation. A deep-integrated model based on the Gaussian Naive Bayes algorithm was built to differentiate AD from non-AD, using the combination of the three-dimensional (3D) deep-learning model score and morphological characteristics. The areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to evaluate the model performance. The proposed model was also compared with the subjective assessment of radiologists.Results: After the combination of all the morphological characteristics, our proposed deep-integrated model significantly outperformed the 3D deep-learning model (AUC: 0.948 vs. 0.803 in the internal testing cohort and 0.969 vs. 0.814 in the external testing cohort, both p < 0.05). The accuracy, sensitivity, and specificity of our model reached 0.897, 0.862, and 0.923 in the internal testing cohort and 0.730, 0.978, and 0.554 in the external testing cohort, respectively. The accuracy for AD detection showed no significant difference between our model and the radiologists (p > 0.05).Conclusion: The proposed model presented good performance for AD detection on non-contrast CT scans; thus, early diagnosis and prompt treatment would be available.