Studies on Poly(propylene fumarate-co-caprolactone diol) Thermoset Composites towards the Development of Biodegradable Bone Fixation Devices

The effect of reinforcement in the cross-linked poly(propylene fumarate-co-caprolactone diol) thermoset composites based on Kevlar fibres and hydroxyapatite was studied. Cross-linked poly(propylene fumarate-co-caprolactone diol) was also studied without any reinforcement for comparison. The reinforcing fibre acts as a barrier for the curing reaction leading to longer setting time and lesser cross-link density. The fibre and HA reinforced composites have almost the same compressive strength. Nonreinforced material undergoes greater degree of swelling. Among the reinforced materials, the hydroxyapatite reinforced composite has a much higher swelling percentage than the fibre reinforced one. The studies on in vitro degradation of the cured materials reveal hydrolytic degradation in Ringer's solution and PBS medium during aging. All the three materials are found to swell initially in Ringer's solution and PBS medium during aging and then undergo gradual degradation. Compression properties of these cross-linked composites increase with aging; HA reinforced composite has the highest compressive strength and compressive modulus, whereas the aged fibre-reinforced composite has the least compressive strength and modulus

Keystroke dynamics based user authentication using deep multilayer perceptron

User authentication is an essential factor to protect digital service and prevent malicious users from gaining access to the system. As Single Factor Authentication (SFA) is less secure, organizations started to utilize Multi-Factor Authentication (MFA) to provide reliable protection by using two or more identification measures. Keystroke dynamics is a behavioral biometric, which analyses users typing rhythm to identify the legitimacy of the subject accessing the system. Keystroke dynamics that have a low implementation cost and does not require additional hardware in the authentication process since the collection of typing data is relatively simple as it does not require extra effort from the user. This study aims to propose deep learning model using Multilayer Perceptron (MLP) in keystroke dynamics for user authentication on CMU benchmark dataset. The user typing rhythm from 51 subjects collected based on the static password (.tie5Roanl) typed 400 times over 8 sessions and 50 repetitions per session. The MLP achieved optimum EER of 4.45% compared to original benchmark classifiers such as 9.6% (scaled Manhattan), 9.96% (Mahalanobis Nearest Neighbor), 10.22% (Outlier Count), 10.25% and 16.14% (Neural Network Auto-Assoc). © 2020 by the authors

Studies on the Age, Growth and Mortality Rates of Indian Oil Sardine, Sardinella longiceps Valenciennes, 1847 off Oman Sea, Muscat, Sultanate of Oman

Investigations have been carried out on the age, growth and mortality of sardine Sardinella longiceps based on the length frequency data. The hypothetical asymptotic length (L∞) and growth coefficient (K) were estimated as 220.3 mm and 1.209 yr-1 respectively. The species grows from 155 and 200 mm at the end of 1 and 2 years of life. The life span appeared to be around 2 years of species life. The total (Z), natural (M) and fishing (F) mortalities were represented as 4.11 yr-1, 2.21 yr-1 and 1.91 yr-1 respectively. The exploitation rate (E) was 0.46. The exploitation rate suggested that the stock was below the optimum level of exploitation. As a management plan, there is a need to increase the fishing scale and protecting spawning season to maintain sustainability over time. Keywords: The Hypothetical Asymptotic Length; Growth Coefficient; Mortalities; Exploitation Rate; Sustainability

Application of Machine Learning to User Behavior-Based Authentication in Smartphone and Web

Authentication is the preliminary security mechanism employed in the information system to identify the legitimacy of the user. With technological advancements, hackers with sophisticated techniques easily crack single-factor authentication (username and password). Therefore, organizations started to deploy multi-factor authentication (MFA) to increase the complexity of the access to the system. Despite the MFA increasing the security of the digital service, the usable security should be given equal importance. The user behavior-based authentication provides a means to analyze the user interaction with the system in a non-intrusive way to identify the user legitimacy. This chapter presents a review of user behavior-based authentication in smartphones and websites. Moreover, the review highlights some of the common features, techniques, and evaluation criteria usually considered in the development of user behavior profiling

Catch trend of the commercial trawl fisheries of Rameswaram

The present report summarises the catch details of some of the commercially important fishes landed by the trawlers operating from Rameswaram during the years 1980 and 1981. The silverbellies, formed the major group, indicating the availability of silverbellies in large quantities throughout the year, followed by Elasmobranchs and Penaeid prawns in the landings. Quarterrwise landings of trawlnets and monthwise landings of prawns and other groups were portrayed

Risk prediction in life insurance industry using supervised learning algorithms

Risk assessment is a crucial element in the life insurance business to classify the applicants. Companies perform underwriting process to make decisions on applications and to price policies accordingly. With the increase in the amount of data and advances in data analytics, the underwriting process can be automated for faster processing of applications. This research aims at providing solutions to enhance risk assessment among life insurance firms using predictive analytics. The real world dataset with over hundred attributes (anonymized) has been used to conduct the analysis. The dimensionality reduction has been performed to choose prominent attributes that can improve the prediction power of the models. The data dimension has been reduced by feature selection techniques and feature extraction namely, Correlation-Based Feature Selection (CFS) and Principal Components Analysis (PCA). Machine learning algorithms, namely Multiple Linear Regression, Artificial Neural Network, REPTree and Random Tree classifiers were implemented on the dataset to predict the risk level of applicants.Findings revealed that REPTree algorithm showed the highest performance with the lowest mean absolute error (MAE) value of 1.5285 and lowest root-mean-squared error (RMSE) value of 2.027 for the CFS method, whereas Multiple Linear Regression showed the best performance for the PCA with the lowest MAE and RMSE values of 1.6396 and 2.0659, respectively, as compared to the other models

Predicting 30-day hospital readmission for diabetes patients using multilayer perceptron

Hospital readmission is considered a key metric in order to assess health center performances. Indeed, readmissions involve different consequences such as the patient's health condition, hospital operational efficiency but also cost burden from a wider perspective. Prediction of 30-day readmission for diabetes patients is therefore of prime importance. The existing models are characterized by their limited prediction power, generalizability and pre-processing. For instance, the benchmarked LACE (Length of stay, Acuity of admission, Charlson comorbidity index and Emergency visits) index traded prediction performance against ease of use for the end user. As such, this study propose a comprehensive pre-processing framework in order to improve the model's performance while exploring and selecting a prominent feature for 30-day unplanned readmission among diabetes patients. In order to deal with readmission prediction, this study will also propose a Multilayer Perceptron (MLP) model on data collected from 130 US hospitals. More specifically, the pre-processing technique includes comprehensive data cleaning, data reduction, and transformation. Random Forest algorithm for feature selection and SMOTE algorithm for data balancing are some example of methods used in the proposed pre-processing framework. The proposed combination of data engineering and MLP abilities was found to outperform existing research when implemented and tested on health center data. The performance of the designed model was found, in this regard, particularly balanced across different metrics of interest with accuracy and Area under the Curve (AUC) of 95% and close to the optimal recall of 99%

Sardines of the Gulf of Mannar ecosystem - fishery and resource characteristics of major species

Fishery, species diversity and resource characteristics of exploited sardine resources of the genera Sardinella were studied during 2000-2008. Sardines were exploited by sardine gillnets, trawls and shore seine. Annual average production for the period was 20,249 t. They formed about one fourth of the total marine fish production (77,443 t) of the region. Fishery was supported by eight species dominated by Sardinella gibbosa, followed by S. sirm, S. albella and S. longiceps. Fishery fluctuated widely with declining trend over the years mainly due to reduction in the fishing effort following destruction of traditional crafts and gears during the Tsunami in 2004. Oilsardine in the fishery registered an increasing trend with wide annual fluctuation during the period. Stock assessment studies show that the exploitation rate of major species ranged between 0.45 and 0.62, against the EMSY value of 0.72 and 0.86. This indicated that sardine resource as a whole is under-exploited, offering considerable scope for enhancing their yield. Despite contributing a higher share to marine fish production, sardines, along with other small pelagics, play a vital ecological role in sustaining the stock and fishery of other predatory groups especially large pelagics by providing them a major share of their forage. They support 46 to 87% of the total food of pelagic predators and 14 to 29% of demersal predators