Live Memory Forensic Analysis

The live memory image acquired in live forensics is always view in terms of integrity and reliability when presented as evidence. In this work, I describe how evidence like live memory obtained from physical memory image (RAM) and trustworthiness of evidence is studied. The evidence in live memory image can be taken as how accurately the memory image of RAM shows the real memory of the target machine. Based on a live memory analysis, investigator can test memory acquisition tool and after that live memory image is analyzed. Then, I describe the part of live memory analysis in the digital cyber forensics process and its use to address many challenges of the digital forensic investigation. In this work, I provide a method to overcome these problems. I highlight at some of the existing methods to live memory analysis. This work is done using acquisition and analysis tools. DOI: 10.17762/ijritcc2321-8169.15055

Mathematical Validation of Experimentally Optimised Parameters Used in a Vibration-Based Machine-Learning Model for Fault Diagnosis in Rotating Machines

Mathematical models have been widely used in the study of rotating machines. Their application in dynamics has eased further research since they can avoid time-consuming and exorbitant experimental processes to simulate different faults. The earlier vibration-based machine-learning (VML) model for fault diagnosis in rotating machines was developed by optimising the vibration-based parameters from experimental data on a rig. Therefore, a mathematical model based on the finite-element (FE) method is created for the experimental rig, to simulate several rotor-related faults. The generated vibration responses in the FE model are then used to validate the earlier developed fault diagnosis model and the optimised parameters. The obtained results suggest the correctness of the selected parameters to characterise the dynamics of the machine to identify faults. These promising results provide the possibility of implementing the VML model in real industrial systems

Psychosocial aspects of changes during adolescence among school going adolescent Indian girls

Background: Physical as well as psychological changes during adolescence create a state of physiological stress that must be coped with. This study was undertaken to study the psychosocial aspects of changes associated with adolescence among school going girls.  Methods: A predesigned questionnaire was administered to students of class VI to XII prior to a talk on ‘Adolescent health’ in two urban schools of Bhopal. The questions were directed at understanding the psychosocial aspects of behavior among the girls during adolescence while they cope with changes of adolescence.  Results: A total of 414 schoolgirls from classes VI-XII participated in the study. Their mean age was 14.4years [SD 2.01; Range 10-18 years]. Of them, 277 reported having attained menarche, the mean age at menarche being 12.7 years [SD 1.52]. Almost 63% of girls had knowledge about menstruation before attaining menarche. Majority of them had learned about it from their mother (41%). Nearly one third (30.6%) of girls were not comfortable with the bodily changes of adolescence; 41% reported feeling anxious and 26.4% reported suffering from low self-esteem. Excessive irritability was reported by 47% of girls; undue anger by 51.4%, and 34.7% felt uncomfortable interacting with people. One third of girls had frequent arguments with parents. Almost 80% of girls found their parents supportive.  Conclusions: A good proportion of adolescent girls appear to be in need for counseling and support for optimally coping with the bodily as well as psychological changes of adolescence. This preliminary study unveils the need for more widespread and regular Adolescent School health programs for increasing awareness and support services

Parameters Optimisation in the Vibration-based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines

Artificial intelligence (AI)-based machine learning (ML) models seem to be the future for most of the applications. Recent research effort has also been made on the application of these AI and ML methods in the vibration-based faults diagnosis (VFD) in rotating machines. Several research studies have been published over the last decade on this topic. However, most of the studies are data driven, and the vibration-based ML (VML) model is generally developed on a typical machine. The developed VML model may not predict faults accurately if applied on other identical machines or a machine with different operation conditions or both. Therefore, the current research is on the development of a VML model by optimising the vibration parameters based on the dynamics of the machine. The developed model is then blindly tested at different machine operation conditions to show the robustness and reliability of the proposed VML model