Modelling and design of new stainless-steel welding alloys suitable for low-deformation repairs and restoration processes

The plasticity associated with low-temperature martensitic transformation can be exploited to reduce the stresses developed due to thermal contraction of the weldments. The key feature of stress-mitigating welding alloys is their transformation from austenite to martensite at low temperatures e.g. ideally close to ambient temperature. Thermodynamics databases (MTDATA and SGTE) were used to model and design new welding alloys with low martensitic transformation temperatures (Ms) around 200 °C. The modelling, conducted in this work, was based on this assumption that martensitic transformation starts at a certain temperature when the free-energy change for austenite to transform to ferrite reaches a critical value. Since martensitic transformation is a diffusion-less process, the change in free-energy vs. temperature was calculated for the austenite and ferrite phases with the same composition. Three prototype welding alloys, CamAlloys 4 & 5 and OpenAlloy 1, were successfully designed and made in the University of Cambridge (UK) and the Open University (UK). The design of these alloys was purely based on thermodynamics equations. Comprehensive characterisation, examinations and mechanical tests showed this family of alloys could substantially reduce contraction-induced deformations in stainless steel weldments. One of the applications of these alloys is in the repair and restoration of damaged stainless-steel components

Biomechanical Modeling for Lung Tumor Motion Prediction during Brachytherapy and Radiotherapy

A novel technique is proposed to develop a biomechanical model for estimating lung’s tumor position as a function of respiration cycle time. Continuous tumor motion is a major challenge in lung cancer treatment techniques where the tumor needs to be targeted; e.g. in external beam radiotherapy and brachytherapy. If not accounted for, this motion leads to areas of radiation over and/or under dosage for normal tissue and tumors. In this thesis, biomechanical models were developed for lung tumor motion predication in two distinct cases of lung brachytherapy and lung external beam radiotherapy. The lung and other relevant surrounding organs geometries, loading, boundary conditions and mechanical properties were considered and incorporated properly for each case. While using material model with constant incompressibility is sufficient to model the lung tissue in the brachytherapy case, in external beam radiation therapy the tissue incompressibility varies significantly due to normal breathing. One of the main issues tackled in this research is characterizing lung tissue incompressibility variations and measuring its corresponding parameters as a function of respiration cycle time. Results obtained from an ex-vivo porcine deflated lung indicated feasibility and reliability of using the developed biomechanical model to predict tumor motion during brachytherapy. For external beam radiotherapy, in-silico studies indicated very significant impact of considering the lung tissue incompressibility on the accuracy of predicting tumor motion. Furthermore, ex-vivo porcine lung experiments demonstrated the capability and reliability of the proposed approach for predicting tumor motion as a function of cyclic time. As such, the proposed models have a good potential to be incorporated effectively in computer assisted lung radiotherapy treatment systems

Representing Increasing Virtual Machine Security Strategy in Cloud Computing Computations

This paper proposes algorithm for Increasing Virtual Machine Security Strategy in Cloud Computing computations. Imbalance between load and energy has been one of the disadvantages of old methods in providing server and hosting, so that if two virtual severs be active on a host and energy load be more on a host, it would allocated the energy of other hosts (virtual host) to itself to stay steady and this option usually leads to hardware overflow errors and users dissatisfaction. This problem has been removed in methods based on cloud processing but not perfectly, therefore,providing an algorithm not only will implement a suitable security background but also it will suitably divide energy consumption and load balancing among virtual severs. The proposed algorithm is compared with several previously proposed Security Strategy including SC-PSSF, PSSF and DEEAC. Comparisons show that the proposed method offers high performance computing, efficiency and consumes lower energy in the network

Development of a patient-specific finite element model of the transcatheter aortic valve implantation (TAVI) procedure

Transcatheter Aortic Valve Implantation (TAVI) is a procedure developed for replacing the defective aortic valve of a patient as an alternative to open heart Surgical Aortic Valve Replacement (SAVR). In the TAVI procedure a prosthetic valve, which is assembled on to a stent, is crimped and delivered to the patient's aortic root site through several available percutaneous means. The percutaneous nature of TAVI, which is its core advantage in comparison to other SAVR procedures, can however also be its main disadvantage. This is due to lack of direct access to the calcified leaflets, and hence reliance on the host tissue for the proper positioning and anchorage of the deployed prosthetic valve. Therefore, it is desired to have a preoperative quantitative understanding of patient-specific biomechanical interaction of the stent and the native valve to be able to maximise the chance of success of the procedure. The aim of this study was to develop a patient-specific Finite Element (FE) model of the Transcatheter Aortic Valve Implantation (TAVI) procedure for two patients, using a model of the 23 mm percutaneous prosthetic aortic valve developed by Strait Access Technologies (SAT), for the purpose of its post-operative performance. In this regard, the image processing software ScanIP was used to extract the 3D models of the patient-specific aortic roots and leaflets from the provided Multi-Slice Computer Tomography (MSCT) images of the patients. An anisotropic hyperelastic material model was implemented for the roots and leaflets, using two and one families of collagen fibres for their tissues respectively. The stent is made of a cobalt-chromium alloy and its mechanical response was modelled as an isotropic elastoplastic material, with a linear elastic initial response, followed by plastic behaviour with isotropic hardening. The prosthetic leaflets are made of polymer and were modelled as an isotropic hyperelastic material, using the provided experimental test data. The results for the first patient showed that the stent maintained its structural integrity after deployment, and successfully pushed the native leaflets back to keep the aortic root clear of all impediments. No obstruction of the coronary ostia was observed, and prosthetic leaflets were seen to function normally. The stent radial recoil was calculated to be between 2 to 4.28 % after deployments. Its foreshortening was calculated to be approximately 20%. The stent was observed to move back and forth by approximately 3 mm in the last simulation step in which cardiac cycle pressure were applied to the aortic root and prosthetic leaflets. Also, two openings were observed between the stent and aortic root wall during this simulation step, which indicates the possibility of paravalvular leakage. From the second patient simulation, it was observed that the 23 mm stent was not a good choice for this patient, and will cause severe damage or tissue tearing. The maximum principal stress in the aortic root and valve tissues were observed to follow approximately the defined collagen fibre directions

Neural network modelling of hot deformation of austenite

The hot deformation behaviour of austenite in steels is a complicated process which depends on chemical composition, microstructure, temperature and strain rate. While many models have been developed to represent the flow stress as a function of these variables, it is not yet possible to predict the behaviour for a new alloy. Linear regression techniques are not capable of representing the data, however, neural networks are capable of modelling highly non-linear data. A neural network model was developed in this work using a large database of various steels. The model allows the calculation of error bars that depend upon the position of a prediction in the input space and the level of perceived noise in the data. The validity of the model was evaluated by comparing its outputs against those of the six carbon-manganese steels with different compositions

Evaluation of a Sentinel Lymph Node Biopsy with Patent Blue in Locally Advanced Gastric Cancer

Background: A sentinel lymph node (SLN) biopsy is an interesting issue in the field of surgical oncology and has recently been introduced to the treatment of gastric cancer. The purpose of this study is to assess accuracy, sensitivity, specificity, and false negative rates (FNRs) of SLN biopsies, and to ascertain whether or not this procedure is useful for locally advanced gastric cancer.Methods: From December 2013 to March 2014, 22 patients with gastric cancer were enrolled in this study. After laparotomy, patent blue was injected around the tumor subserosaly, resection was then done, and SLNs were detected on a back table. Afterward, D2 dissection was carried out. Finally, SLNs and other specimens were submitted for permanent pathology.Results: SLNs were detected in 20 of 22 patients. The total number of SLNs was 87. SLNs were positive in 7 patients, and the total number of positive SLNs was 17. In three patients, the SLNs were negative, whereas other LNs were positive, with an FNR of 15%. 18 patients received neoadjuvant. Complete pathologic responses with negative LNs were seen in 3 patients. Accuracy, sensitivity, specificity, and negative predictive values were 80%, 66%, 90%, and 76%, respectively.Conclusions: This research demonstrated that SLN mapping in advanced gastric cancer is an appropriate method with acceptable levels of accuracy, sensitivity, and negative predictive values, even in those patients who received neoadjuvant treatment

Joining ceramics to metals using metallic foam

A general method for brazing ceramics to metals using a compliant metallic foam as a buffer layer has been developed. Using stainless steel foams, bonds between alumina and 316 stainless steel with shear strengths up to 33 MPa have been achieved. The resultant ductility enhances the resistance of the joint to thermal cycling; AlN-Inconel 600 bonds exhibited good thermal shock resistance. Alumina - stainless steel bonds withstood more that 60 thermal cycles between 200 and 800°C in air