There are a number of methods for measuring propeller pitch, but there could still be problems for many fishing boat builders because of their lack of necessary professional equipment. This paper presents a method of propeller pitch measuring based on photogrammetry and CAD (Computer-Aided Design). This method consists of three stages. The first stage is to take a series of 2D image photographs of the propeller using a smart phone. The second stage consists of processing these 2D images using photogrammetry software to create a 3D virtual model of the propeller. A CAD program is used to measure the pitch at different radii of each blade of the 3D virtual model in the third stage. In this study, several propellers for fishing boats were measured and the results were compared to those archived by using an EDM (Electrical Discharge Machining) machine and by a qualified professional worker using simple tools. The measurement results showed that the proposed method would be acceptable for measuring pitches of propellers of fishing boats.

In recent years, 3D printed arm casts can replace traditional arm casts to treat bones fractures. 3D printed arm cast modelling often uses professional 3D scanning systems to capture 3D data of the arm. These systems are very expensive and may not be available in many hospitals. In order to overcome this disadvantage, inexpensive methods should be developed. This paper introduces a new data collection method based on smartphones. The photos of an arm were taken with a smartphone camera using some special techniques that could facilitate the process of image processing and 3D modelling in Agisoft Metashape and CATIA. To validate the proposed method, the photogrammetric model was compared with the scanned model (obtained by a low cost scanner) in GOM Inspect. Besides, a fit check of real 3D printed arm casts attached on the volunteer's forearm was also performed. The test results indicate that the photogrammetric model could be used as raw data for 3D arm modelling.

Controlling potential risks in Petroleum Industry is one of the most difficult tasks for geologists, reservoir engineers and others. Evaluating oil and gas reserves becomes riskier and less accurate for fields which have complex geologic structure and high heterogeneity. In these cases, correlation relationship between dependent reservoir parameters will be represented better and more exactly with non-linear regression models.The "box" method is introduced in this research as an improved technique in handling these more accurate non-linear regression models compared to conventional method -Spearman's rank order correlation methodwhich is available in many commercial Monte Carlo simulators. The conventional method is just correct in uniform and homogeneous reservoirs whereas the "box" method is better for complex and heterogeneous reservoirs.To get the most accurate estimation of oil and gas reserves, a computer program using the above improved technique along with Monte Carlo simulation method was written. Some randomly generated data that were similar to data from a specific heterogeneous reservoir were also considered. And exhaustive simulation runs were performed to compare the results from written computer program to those from a commercial Monte Carlo simulation package. It was seen that their outputs were different due to different methods used.Regarding such exactly estimated amount of oil and gas from written computer program, project designing team planned field development more accurately and efficiently. This very important factor led to a successful production project.

OBJECTIVE: Laparoscopic radical nephrectomy (LRN) has been suggested as the standard care for cancer patients in the T1-2 stage. However, whether this advanced technique is most indicated suitable for renal tumors higher than T3a and N1 is unclear, especially in different regions and countries, such as the difference between European and Asia. METHODS: From 2013 to 2021, the data of pathologically diagnosed renal cell carcinoma (RCC) patients who received laparoscopic retroperitoneal radical nephrectomy was subjected to the present study. RESULTS: Overall, all the registered Vietnamese patients were eligible for the study. The average operative time was 86.8 ± 21.2 min and the percentage number of patients in stages 1, 2, and 3 were 134 (70.2%), 30 (15.7%), and 27 (14.1%), respectively. Patients in the 3rd stage had a significantly longer operative time than stages 1–2 (p = 0.0001). No Lymph-node dissection (LND) was recorded in 10 patients (5.2%), limited LND in 163 patients (85.3%), regional LND in 13 patients (6.8%), extended LND (eLND) in 5 patients (2.6%). eLND showed only prolongation of operative time (p = 0.000), however, did not increase intraoperative complications as well as prolonged the duration of analgesia and hospital stay when compared with the other 2 groups (p = 0.82, 0.85, 0.91). Mean follow-up time: 42.3 ± 24.7 months. The 5-year recurrent free survival and 5-year overall survival of the stage 1, 2, 3 were: 98.3%, 100%, 87.8%, and 98.9%, 100%, and 91.3%, respectively. (p = 0.0011, p = 0.0082). CONCLUSION: Retroperitoneal LRN could be an important technique in improving long-term oncological outcomes for Vietnamese patients, especially in the stage of T1-3N0-1M0 tumors. Radical retroperitoneal nephrectomy is safe and technically feasible as well as providing favorable long-term oncological outcomes for stage T1-2-3aN1M0 RCC.