Application Analysis of 3D Printing Technology in Design Field: Taking Shoe Design as an Example

The development of 3D technology has brought opportunities and challenges to the footwear industry because people’s living standards have been improving due to economic development, and people have higher requirements for the design of fashion shoes and boots. The use of 3D printing technology in the design of fashion shoes and boots can enable faster molding of footwear products, enrich the shape of footwear, and meet people’s aesthetic needs for fashion shoes. In this paper, we firstly describe the advantages of 3D printing molding shoe models and then use 3D laser foot scanning and measuring instrument to scan and obtain the cloud map of shoe lasts and foot-related data. Secondly, we realize the digital management of shoe lasts by establishing the database of solid models. On this basis, we apply the technology of least squares support vector machine improvement algorithm to make partial modifications to the lasts according to the need to leave the appropriate helper and foot lining degrees. Finally, based on this technology, we apply the least squares support vector machine improvement algorithm technology to make partial modifications to the last shape according to the need for appropriate helper and foot lining degrees to realize the process of shoe last redesign. The last model and role model can also be produced by 3D printing technology, which can be used as a mold to facilitate the processing of the cut last 2D unfolding material for shoe and boot production later. Therefore, the article studies and analyzes the design and manufacturing process of digital shoe lasts based on individual foot shape and uses CAD/CAM technology to realize the digitalization of shoe last design.

Application of the Sino US Financial Technology in Banks

Objectives: With the rise and development of Internet finance, the application of Sino US financial technology in the banking field is becoming more and more widely. Methods: In this study, for the data collection of bank customer deposits, data mining and decision tree analysis algorithms were used to classify bank customers. Results: The classification accuracy of the traditional algorithm was low, so the optimization algorithm Adaboost and the random forest improvement algorithm were proposed in this paper. The simulation effects of its application in data combination show that the classification effect of the optimization algorithm is obviously better than the traditional classification algorithm. Conclusion: The results of this study can help banks gain customers and reduce expenditures.

Severe Brain Atrophy Predicts Poor Clinical Outcome After Endovascular Treatment of Acute Basilar Artery Occlusion: An Automated Volumetric Analysis of a Nationwide Registry

Background: Brain atrophy globally reflects the effects of preexisting risk factors and biological aging on brain structures and normally predicts poor outcomes in anterior circulation stroke. However, comparing with these patients, acute basilar artery occlusion (ABAO) impairs infratentorial regions frequently and might benefit from brain atrophy due to the resulting residual space to reduce tissue compression and thus improve prognosis, which raises doubts that current understandings for prognostic roles of brain atrophy are also applicable for ABAO. Therefore, this study aims to evaluate brain atrophy automatically from CT images and investigates its impact on outcomes of ABAO following endovascular treatment (EVT).Methods: A total of 231 ABAO who underwent EVT from the BASILAR registry were enrolled. Brain atrophy was quantified as the ratio of brain parenchymal volume to cerebrospinal fluid volume on baseline CT. The primary outcome was the modified Rankin Scale (mRS) score at 3 months.Results: The frequency of favorable outcomes (90-day mRS ≤ 3) was significantly lower in the severe atrophy group (P = 0.014). Adjusted logistic models revealed that severe brain atrophy was significantly negatively associated with favorable outcome incidence (P = 0.006), with no relationship with either in-hospital or 90-day overall mortality (all P > 0.05). Adding a severe atrophy index into the baseline model obviously enhanced its discriminatory ability in predicting the outcome by obviously increasing areas under the receiver operating characteristic curve, net reclassification improvement algorithm, and integrated discrimination improvement algorithm values (all P < 0.05).Conclusion: Severe brain atrophy did not improve in-hospital or overall mortality but impaired the long-term recovery after EVT. This objective and automated marker has the potential to be incorporated into decision-support methods for treating ABAO.

Magnetic Resonance Image under Variable Model Algorithm in Diagnosis of Patients with Spinal Metastatic Tumors

The aim of this study was to explore the adoption of the variable model algorithm in magnetic resonance imaging (MRI) image analysis and evaluate the effect of the algorithm-based MRI in the diagnosis of spinal metastatic tumor diseases. 100 patients with spinal metastatic tumors who were treated in hospital were recruited as the research objects. All patients were randomly divided into the experimental group (MRI image analysis based on variable model) and the control group (conventional MRI image diagnosis), and the MRI of the experimental group was segmented using the conventional algorithm with variable model and the improved algorithm with GVF force field. The accuracy index (Dice coefficient D) values were used to evaluate the vertebral segmentation effect of the improved variable model algorithm with the introduction of GVF force field, and the recognition rate, sensitivity, and specificity indexes were used to evaluate the effects of the two algorithms on the recognition of MRI image features of spinal metastatic tumors. The results showed that the mean D value of the variable model improvement algorithm for the segmentation of five vertebrae of spinal metastatic tumors was significantly improved relative to the conventional variable model algorithm, and the difference was statistically significant ( P < 0.05 ). At the number of 80 iterations, the recognition rate, sensitivity, and specificity of MRI image segmentation of the traditional variable model algorithm processing group were 89.32%, 74.88%, and 86.27%, respectively, while the recognition rate, sensitivity, and specificity of MRI image segmentation of the variable model improvement algorithm processing group were 97.89%, 96.75%, and 96.45%, respectively. The results of the latter were significantly better than those of the former, and the differences were statistically significant ( P < 0.05 ); and the comparison of MRI images showed that the variable model improvement algorithm was more rapid and accurate in identifying the focal sites of patients with spinal metastases. The accuracy of MRI images based on the variable model algorithm increased from 69.5% to 92%, and the difference was statistically significant ( P < 0.05 ). In short, MRI image analysis based on the variable model algorithm had great adoption potential in the clinical diagnosis of spinal metastatic tumors and was worthy of clinical promotion.

The Clampdown Effect: On The Expulsion of Super-Intelligence

There exists an implicit potential limitation in every physical discoveries that has been implemented and understood. However, the limitations can be bounded within a safe limit to prevent any constructing theory to be free from errors. As, it&rsquo;s the inert nature of the humans, to go far beyond the scope of experimental findings in order to pursue any studies with the sole help of logical reasoning and mathematics, the argument can be prevailed in the form of WEAK Clampdown Effect &amp; STRONG Clampdown Effect. More, the theories are constructed out of physical nature, more the theory gets hypothetical without any finding evidence, but that does or doesn&rsquo;t actually justify the phenomenon, that too with the more increment of KARDASHEV Scale, more moderate ways of experimentation got developed curbing down the limitations within the human limit of &lsquo;ERRORS&rsquo;, that does can be neglected by approximation. Relationship being cross-judgmental on the basis of the computational limits and calculation accuracy, leading to a soft singularity, as a warning, that if computer powers cannot be checked on the basis of error approximations, then this may lead to the hitting of a hard singularity, that in phase with the forbidden gap (or after the optimum limit that arises at the core constraints of nature) to prevent the computation being carried off with respect to super-intelligence machines that are cognitive capability oriented future computers responsible for self growth &amp; reproduction with more improvement algorithm, restricting all forms of humanity &amp; constraints the human growth by virtue of limiting capacities of the humans as compared to computers.