Developing a Support Vector Regression (SVR) Model for Prediction of Main and Lateral Bending Angles in Laser Tube Bending Process.

The laser tube bending process (LTBP) is a new and powerful manufacturing method for bending tubes more accurately and economically by eliminating the bending die. The irradiated laser beam creates a local plastic deformation area, and the bending of the tube occurs depending on the magnitude of the heat absorbed by the tube and its material characteristics. The main bending angle and lateral bending angle are the output variables of the LTBP. In this study, the output variables are predicted by support vector regression (SVR) modeling, which is an effective methodology in machine learning. The SVR input data is provided by performing 92 experimental tests determined by the design of the experimental techniques. The measurement results are divided into two sub-datasets: 70% for the training dataset, and 30% for the testing dataset. The inputs of the SVR model are process parameters, which can be listed as the laser power, laser beam diameter, scanning speed, irradiation length, irradiation scheme, and the number of irradiations. Two SVR models are developed for the prediction of the output variables separately. The SVR predictor achieved a mean absolute error of 0.021/0.003, a mean absolute percentage error of 1.485/1.849, a root mean square error of 0.039/0.005, and a determination factor of 93.5/90.8% for the main/lateral bending angle. Accordingly, the SVR models prove the possibility of applying SVR to the prediction of the main bending angle and lateral bending angle in LTBP with quite an acceptable accuracy.

Intelligent temperature modeling in robotic cortical bone milling process based on teaching-learning-based optimization algorithm.

Bone milling is one of the most important and sensitive biomechanical processes in the field of medical engineering. This process is used in orthopedic surgery, dentistry, treatment of fractures, and bone biopsy. The use of automatic numerical control surgical milling machines has revolutionized this procedure. The most important possible complication in bone surgery is the rise of temperature above permissible range and the formation of thermal necrosis or cell death in bone tissue. In the present article, a study on the design of experiment is first conducted by considering the rotational speed of the utilized tool, feed rate, depth of cut and tool diameter as the most important input factors of this process. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to model and estimate the temperature behavior in the process of robotic bone milling. The optimal parameters of the ANFIS system are obtained using teaching-learning-based optimization (TLBO) algorithm. In order to model the process behavior, the results of experiments are used for the training (75% of the data) and testing (25% of the data) of the adaptive inference system. The accuracy of the obtained model is investigated via different plots, and statistical criteria, including root mean square error, correlation coefficient, and mean absolute percentage error. The findings show that the ANFIS network successfully predicts the temperature in the automatic bone milling process. In addition, the network error in estimating the temperature of the automatic bone milling process in the training and test section is equal to 1.74% and 3.17%, respectively.

Glioblastoma multiforme: a glance at advanced therapies based on nanotechnology.

Glioblastoma multiforme (GBM, grade IV) is the most common malignant and invasive central nervous system tumor with poor survival outcome. Various pathogenesis signatures such as genetic mutation, hypoxia, necrosis and neo-angiogenesis are involved in GBM. Standard treatment includes surgical resection along with radiation therapy and temozolomide (TMZ) chemotherapy that do not improve the overall survival of patients. In this review, we focused on the diagnosis, risk factors and novel therapies, using advanced therapies such as nanotechnology in drug delivery, gene therapy and hyperthermia that have promising roles in the treatment of aggressive brain tumors.

Preclinical Evaluation of 68Ga-MAA from Commercial Available 99mTc-MAA Kit.

99mTc-Macroaggregated Albumin (99mTc-MAA) has been used as a perfusion agent. This study described development of the 68Ga-MAA via commercially available kits from Pars-Isotopes Company as a 99mTc-MAA kit. 68Ge/68Ga generator was eluted with suprapure HCl (0.6 M, 6 mL) in 0.5 mL fractions. The two fractions with the highest 68GaCl3 activity were generally used for labeling purposes. After labeling, the final product was centrifuged 2 times to purify the solution. Five rats were sacrificed at each exact time interval (from 15 min to 2 h post injection) and the percentage of injected dose per gram (%ID/g) of each organ was measured by direct counting from 11 harvested organs of rats. The RTLC showed that labeling yields before centrifuges were 90% and 95% for Pars-Isotopes and GE kits, respectively and after centrifuges, they became 100%. The microscopic size examination showed a shift in the particle sizes post centrifuges and the biodistribution data revealed the efficiency and benefits of centrifuges in terms of preventing the of liver and bone marrow uptakes especially for Pars-Isotopes kits. Our results showed that after centrifuges of the final product, the lung uptakes increased from 89% to more than 97% of %ID/g after 5 min post injections. The whole procedure took less than 25 min from elution to the final product. Since 99mTc-MAA remained longer than 68Ga-MAA in the lung and 68Ga-MAA showed better image qualities, using 68Ga-MAA is recommended.

Antimicrobial properties of highly fluorinated silver(I) tris(pyrazolyl)borates.

Highly fluorinated tris(pyrazolyl)borate ligand [HB(3,5-(CF3)2Pz)3]- has been used in the isolation of air- and light-stable silver complex, [HB(3,5-(CF3)2Pz)3]Ag(OSMe2). It is a monomeric tetrahedral silver complex with an O-bonded dimethylsulfoxide ligand. The silver adduct [HB(3,5-(CF3)2Pz)3]Ag(OSMe2) and the related [HB(3,5-(CF3)2Pz)3] Ag(THF) (where OSMe2 = dimethyl sulfoxide; THF = tetrahydrofuran) show good antibacterial activity, and their antimicrobial efficacy against Staphylococcus aureus is greater than those of AgNO3 and silver sulfadiazine.