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The failure mechanism of thermal gate oxide in silicon carbide (SiC) power metal oxide semiconductor field effect transistors (MOSFETs), whether it is field-driven breakdown or charge-driven breakdown, has always been a controversial topic. Previous studies have demonstrated that the failure time of thermally grown silicon dioxide (SiO2) on SiC stressed with a constant voltage is indicated as charge driven rather than field driven through the observation of Weibull Slope β. Considering the importance of the accurate failure mechanism for the thermal gate oxide lifetime prediction model of time-dependent dielectric breakdown (TDDB), charge-driven breakdown needs to be further fundamentally justified. In this work, the charge-to-breakdown ( Q B D ) of the thermal gate oxide in a type of commercial planar SiC power MOSFETs, under the constant current stress (CCS), constant voltage stress (CVS), and pulsed voltage stress (PVS) are extracted, respectively. A mathematical electron trapping model in thermal SiO2 grown on single crystal silicon (Si) under CCS, which was proposed by M. Liang et al., is proven to work equally well with thermal SiO2 grown on SiC and used to deduce the Q B D model of the device under test (DUT). Compared with the Q B D obtained under the three stress conditions, the charge-driven breakdown mechanism is validated in the thermal gate oxide of SiC power MOSFETs. [ABSTRACT FROM AUTHOR]

Although machine learning (ML) has brought new insights into geochemistry research, its implementation is laborious and time‐consuming. Here, we announce Geochemistry π, an open‐source automated ML Python framework. Geochemists only need to provide tabulated data and select the desired options to clean data and run ML algorithms. The process operates in a question‐and‐answer format, and thus does not require that users have coding experience. After either automatic or manual parameter tuning, the automated Python framework provides users with performance and prediction results for the trained ML model. Based on the scikit‐learn library, Geochemistry π has established a customized automated process for implementing classification, regression, dimensionality reduction, and clustering algorithms. The Python framework enables extensibility and portability by constructing a hierarchical pipeline architecture that separates data transmission from the algorithm application. The AutoML module is constructed using the Cost‐Frugal Optimization and Blended Search Strategy hyperparameter search methods from the A Fast and Lightweight AutoML Library, and the model parameter optimization process is accelerated by the Ray distributed computing framework. The MLflow library is integrated into ML lifecycle management, which allows users to compare multiple trained models at different scales and manage the data and diagrams generated. In addition, the front‐end and back‐end frameworks are separated to build the web portal, which demonstrates the ML model and data science workflow through a user‐friendly web interface. In summary, Geochemistry π provides a Python framework for users and developers to accelerate their data mining efficiency with both online and offline operation options. Plain Language Summary: Geochemistry π is a helpful tool for scientists who work with geochemical data. One of its standout features is its simplicity. Scientists can use the tool to perform machine learning (ML) on the tabular data by answering a series of questions about what they want to discover. The tool does the rest by using advanced ML techniques to uncover insights from the data. Even scientists without coding skills can use Geochemistry π effectively. This tool is built on a reliable library called scikit‐learn, ensuring that it works well with different ML methods. It is also flexible, allowing researchers to customize it to fit their specific needs. Geochemistry π separates data processing from ML tasks, making it adaptable and expandable. It includes features for continuous training and managing the entire ML process. To prove its effectiveness, Geochemistry π was tested against previous geochemical studies in areas such as regression, classification, clustering, and dimensional reduction. The results showed that it could replicate the findings of these studies accurately. Accessible through a web portal or command line, Geochemistry π is a valuable asset for geochemists and researchers looking to analyze large geochemical data sets. Key Points: Open‐source Python framework for machine learning applications in geochemistryAutomated pipeline for tabular dataQuestion‐and‐answer format obviates the need for coding experience [ABSTRACT FROM AUTHOR]

Background: majority of the time, giant cell tumours (GCT) of the bone develop in the region of the long bones' metaphysis, frequently in contact with the articular cartilage. Their treatment remains controversial because of their high recurrence rate. Many studies have been conducted using various treatment techniques for GCT's resection but less emphasis is given to managing it with curettage and cementation with internal fixation. Aim: Our study aims to evaluate the functional outcome of curettage and cementation with plate fixation in a giant cell tumor of a long bone. Methods: This Interventional study was conducted between November 2020 to October 2021 in our orthopaedics department after clearance from the ethical committee. 27 GCT patients received treatment using extended curettage and cementation followed by plate fixation. All of these tumors were present around the knee: 21 at the distal femur and 6 at the proximal tibia. Preoperative radiological evaluation with standard X-rays showed that the tumour measured a mean 65 × 41 mm, for a mean volume of 70 cm3. Seventy-eight percent of these GCTs were in direct contact with the articular cartilage and 45% extended to the soft tissues as seen on the MRI. All patients were treated with extended curettage and cementation with plate fixation. Follow up was done after 1,3,8,12,24 weeks. All patients continue to be monitored, with none lost to follow-up. Functional evaluation was done using the Musculoskeletal Tumour Society (MSTS) scoring system for the lower extremity Results: The overall MSTS score was excellent in 22 patients (84%), good in 3 patients (11%), fair in 1 patients (0.03%), and poor in 1 patient (0.03%). The overall local recurrence rate was 0.07% (2 cases) and a 10% complication rate. There were no cases of fracture non-union or distant metastasis till 1 year follow up in all patients. Conclusion: GCTs around the knee with associated pathological fractures at diagnosis can be treated with curettage and cementation as cavity filling with cement has its own advantage of cytotoxic effect and mechanical support synergizing with the stability given by the plate to achieve an anatomic reduction and stable fixation. This procedure also has an added advantage of joint preservation in a younger age and varied mega-prosthetic procedures. This technique is a good and equivalent option along with other techniques using extending curettage. [ABSTRACT FROM AUTHOR]

Background: It is essential to determine the optimal treatment for intertrochanteric (IT) fractures due to their high incidence and related public health burden. Debate remains as to whether dynamic hip screws (DHS) or proximal femoral nails (PFNs) are best practices, and our study seeks to gather information relevant to this query. Methods: We undertook a retrospective audit of 90 patients in Rohilkhand Medical College between 2017 to 2021, who received a dynamic hip screw (Group A) or a proximal femoral nail (Group B) in order to compare age, sex, duration of surgery, blood loss, fluoroscopy time, functional outcome based on Harris hip score and radiological outcome was compared. Intraoperative and pre-operative data were collected from the record and patients were followed up for functional outcomes. Results: Out of 90 patients 45 (50%) patients were placed in each group. The average age of the patients was 64.43±17.17 years. Intraoperative blood loss in the PFN group(157.11ml) was significantly less compared to the DHS group (388.44ml), the mean duration of surgery in PFN group was 73.44min and 92.56min in DHS group(P<0.001), a shorter incision in PFN group compared to DHS group. The average limb shortening in both groups was not significant. The patients treated with PFN had better results than DHS when compared to Harris hip score. Conclusion: This study demonstrated PFN was found to be a better implant as compared to DHS, among patients undergoing intertrochanteric fracture fixation via PFN or DHS. [ABSTRACT FROM AUTHOR]

Introduction: Osteoporosis represent a continuum of conditions that present with a decrease in bone mass and disrupted bone microarchitecture most commonly as a result of increased age, menopause and metabolic abnormalities. Diagnosis is made with a DEXA scan having a T-score of >2.5 standard deviations below the peak bone mass of a 25 year old individual. Fracture risk assessment tool (FRAX) is another online tool which is used for probability of neck of femur fracture in an individual upto 10 years. Materials and methods: 806 patients of age group from 40 years to 90 years were selected for the study. 388 were male and 418 were female were included in the study. Bone mineral density was measured by DEXA scan at femoral neck and then FRAX score was calculated. Results: In our study, 20.9% of males and 17.2% females were osteopenic which were in the age group of 40 to 50 years. Incidence of osteoporosis was maximum in 7.2% males being in age group 40 to 50 years and 7.2% females in 51 to 60 years age group. Probability of major osteoporotic fractures was more in four females and there was no probability of osteoporotic fractures in males for upto 10 years. Conclusion: Development of the FRAX tool enables physicians working in primary health care to calculate the future risk of osteoporotic fractures in patients through the integration of a range of clinical risk factors with or without BMD measurements. Nevertheless, the FRAX tool should not replace the detailed clinical evaluation and additional clinical factors that are not currently included in the FRAX models. [ABSTRACT FROM AUTHOR]

Full mouth Rehabilitation (FMR) not only restores the smile and function but also focuses on holistic care of the patient directly or indirectly by boosting the morale and overall quality of life of the person. FMR is practiced by prosthodontist based on their knowledge, practical skills and numerous philosophies of occlusal rehabilitation. The present case report describes the smile designing, overall restoration of function and resultant psychological satisfaction. [ABSTRACT FROM AUTHOR]