Development of an Edible Coating from Okra Mucilage to Preserve the Crispiness in Soft Dough Biscuits Upon Storage

Baked food products are considered to be one of the most popular processed foods in the world. Among all the bakery products, biscuits are the most frequently consumed snack food item. There is a vast and diverse market for biscuits as a leader in ready-to-eat baked goods. Biscuits have long shelf life than other bakery products when stored under proper conditions. But immediately after exposure to the air by opening or damaging the package, biscuits absorb moisture from the air and reduce its crispiness of the biscuits. According to the market survey results, the highest number of respondents (53.7 %) suggested developing an edible coating for biscuits to prevent the loss of crispiness in biscuits upon storage. As well as 98.5 % of respondents prefer edible coatings developed using natural sources. Polysaccharide-based edible coatings maintain the physicochemical, microbiological & sensorial properties of the food. Therefore, okra mucilage was used as the main ingredient for edible coating preparation for biscuits. The coating was applied on biscuits with different coating methods, different baking stages and stored in different environmental conditions to identify the efficiency of the coating. According to the results, a coating applied before the baking stage displayed better moisture barrier properties than that of the coating applied after baking in controlled, semi-controlled, and normal atmospheric environmental conditions. It's responsible for the reduction of moisture absorption of biscuits upon storag

Research Update: Density functional theory investigation of the interactions of silver nanoclusters with guanine

Citation: Dale, B. B., Senanayake, R. D., & Aikens, C. M. (2017). Research Update: Density functional theory investigation of the interactions of silver nanoclusters with guanine. APL Materials, 5(5). doi:10.1063/1.4977795Bare and guanine-complexed silver clusters Agnz (n = 2-6; z = 0-2) are examined using density functional theory to elucidate the geometries and binding motifs that are present experimentally. Whereas the neutral systems remain planar in this size range, a 2D-3D transition occurs at Ag5+ for the cationic system and at Ag42+ for the dicationic system. Neutral silver clusters can bind with nitrogen 3 or with the pi system of the base. However, positively charged clusters interact with nitrogen 7 and the neighboring carbonyl group. Thus, the cationic silver-DNA clusters present experimentally may preferentially interact at these sites. © 2017 Author(s)

Android source code vulnerability detection: a systematic literature review

The use of mobile devices is rising daily in this technological era. A continuous and increasing number of mobile applications are constantly offered on mobile marketplaces to fulfil the needs of smartphone users. Many Android applications do not address the security aspects appropriately. This is often due to a lack of automated mechanisms to identify, test, and fix source code vulnerabilities at the early stages of design and development. Therefore, the need to fix such issues at the initial stages rather than providing updates and patches to the published applications is widely recognized. Researchers have proposed several methods to improve the security of applications by detecting source code vulnerabilities and malicious codes. This Systematic Literature Review (SLR) focuses on Android application analysis and source code vulnerability detection methods and tools by critically evaluating 118 carefully selected technical studies published between 2016 and 2022. It highlights the advantages, disadvantages, applicability of the proposed techniques and potential improvements of those studies. Both Machine Learning (ML) based methods and conventional methods related to vulnerability detection are discussed while focusing more on ML-based methods since many recent studies conducted experiments with ML. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in secure mobile application development while minimizing the vulnerabilities by applying ML methods. Furthermore, researchers can use the discussions and findings of this SLR to identify potential future research and development directions

Influenza epidemiology, vaccine coverage and vaccine effectiveness in sentinel Australian hospitals in 2013: the Influenza Complications Alert Network

The National Influenza Program aims to reduce serious morbidity and mortality from influenza by providing public funding for vaccination to at-risk groups. The Influenza Complications Alert Network (FluCAN) is a sentinel hospital-based surveillance program that operates at 14 sites in all states and territories in Australia. This report summarises the epidemiology of hospitalisations with confirmed influenza, estimates vaccine coverage and influenza vaccine protection against hospitalisation with influenza during the 2013 influenza season. In this observational study, cases were defined as patients admitted to one of the sentinel hospitals, with influenza confirmed by nucleic acid testing. Controls were patients who had acute respiratory illnesses who were test-negative for influenza. Vaccine effectiveness was estimated as 1 minus the odds ratio of vaccination in case patients compared with control patients, after adjusting for known confounders. During the period 5 April to 31 October 2012, 631 patients were admitted with confirmed influenza at the 14 FluCAN sentinel hospitals. Of these, 31% were more than 65 years of age, 9.5% were Indigenous Australians, 4.3% were pregnant and 77% had chronic co-morbidities. Influenza B was detected in 30% of patients. Vaccination coverage was estimated at 81% in patients more than 65 years of age but only 49% in patients aged less than 65 years with chronic comorbidities. Vaccination effectiveness against hospitalisation with influenza was estimated at 50% (95% confidence interval: 33%, 63%, P<0.001). We detected a significant number of hospital admissions with confirmed influenza in a national observational study. Vaccine coverage was incomplete in at-risk groups, particularly non-elderly patients with medical comorbidities. Our results suggest that the seasonal influenza vaccine was moderately protective against hospitalisation with influenza in the 2013 season. This work i

Influenza Vaccine Effectiveness against Hospitalisation with Confirmed Influenza in the 2010-11 Seasons: A Test-negative Observational Study

Immunisation programs are designed to reduce serious morbidity and mortality from influenza, but most evidence supporting the effectiveness of this intervention has focused on disease in the community or in primary care settings. We aimed to examine the effectiveness of influenza vaccination against hospitalisation with confirmed influenza. We compared influenza vaccination status in patients hospitalised with PCR-confirmed influenza with patients hospitalised with influenza-negative respiratory infections in an Australian sentinel surveillance system. Vaccine effectiveness was estimated from the odds ratio of vaccination in cases and controls. We performed both simple multivariate regression and a stratified analysis based on propensity score of vaccination. Vaccination status was ascertained in 333 of 598 patients with confirmed influenza and 785 of 1384 test-negative patients. Overall estimated crude vaccine effectiveness was 57% (41%, 68%). After adjusting for age, chronic comorbidities and pregnancy status, the estimated vaccine effectiveness was 37% (95% CI: 12%, 55%). In an analysis accounting for a propensity score for vaccination, the estimated vaccine effectiveness was 48.3% (95% CI: 30.0, 61.8%). Influenza vaccination was moderately protective against hospitalisation with influenza in the 2010 and 2011 seasons

Advances in the Imaging of Pituitary Tumors

© 2020 Elsevier Inc. In most patients with pituitary adenomas magnetic resonance imaging (MRI) is essential to guide effective decision-making. T1- and T2-weighted sequences allow the majority of adenomas to be readily identified. Supplementary MR sequences (e.g. FLAIR; MR angiography) may also help inform surgery. However, in some patients MRI findings are ‘negative’ or equivocal (e.g. with failure to reliably identify a microadenoma or to distinguish postoperative change from residual/recurrent disease). Molecular imaging [e.g. 11C-methionine PET/CT coregistered with volumetric MRI (Met-PET/MRCR)] may allow accurate localisation of the site of de novo or persistent disease to guide definitive treatment (e.g. surgery or radiosurgery)

The application of ANFIS prediction models for thermal error compensation on CNC machine tools

Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis. A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4 μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system