ENCRYPTION THREE-DIMENSION IMAGE USING TINY ALGORITHM

The development of systems allows providing the capability of using three-dimension (3D) pictures over the internet especially in social media. In previous years, animation pictures and videos are not used in the internet due to the sizes of these two data and need the huge amount of data to work over internet and need supporting program to deal with presenting the data to the users of the internet in either websites or social media. Most of the security over internet used on ciphering text or ciphering images but not cipher video or 3D picture because video and 3D pictures are not used until recently. The huge use of these two types 3D pictures and videos in recent years. It is become an urgent necessary to encrypt these sorts of data. The research will focus on encrypting these types of data by using special algorithm called as Tiny Encryption Algorithm (TEA). This algorithm will be used to encrypt and decrypt 3D pictures and protecting the privacy of this sort of data. The research shows the how-to encode and decode of 3D picture and how to deal with them. The results show the TEA is rapid algorithm in the coding picture and decoding 3D pictures. it is only needing a few portions of time to cipher and decipher 3D pictures. The program that used to test the ciphering and deciphering algorithm was based on MATLAB

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society