WOMEN’S EDUCATION IN INDIA – THE POWER OF A SECOND CHANCE

Education plays a major role in the sustainable development of a nation. The global literacy rate for all people aged 15 and above is 86.3% with the global literacy rate for all males at 90.0% and the rate for all females at 82.7%. The rate varies throughout the world with developed nations having a rate of 99.2% (2013). In many developing countries, despite gains in educational attainment, learning levels are abysmally low, both when compared with developed countries and with national learning standards (Pritchett, 2013). Over 75% of the world's 781 million illiterate adults are found in South Asia, West Asia and sub-Saharan Africa and women represent almost two-thirds of all illiterate adults globally.  (Source: UNESCO Institute of Statistics, 2015). Although India has raised its literacy rate in 2011 to 74.04% from 12% at the time of Independence in 1947, it still lags behind the world average literacy rate of 86%.  Besides this there is a wide gender disparity in the literacy rate in India: effective literacy rates (age 7 and above) in 2011 were 82.14% for men and 65.46% for women (source: Census 2011). One of the main reasons for this is the alarming rate of school dropouts among girls due to culture, custom and poverty. Though women today have achieved many milestones, women’s education has yet to reach its full potential. “The Power of a Second Chance" is an opportunity given to those women who missed out on the first chance in education during their childhood. This research paper, through a study conducted of 145 women school dropouts, attempts to highlight some of the reasons for girls dropping out of school and the impact of lack of education on their social and financial status. It discusses the socio-economic barriers to their progress.  It also makes recommendations on how these women can be empowered with a second chance through literacy programs and income-generating skills.&nbsp

Encapsulation of Soft Computing Approaches within Itemset Mining a A Survey

Data Mining discovers patterns and trends by extracting knowledge from large databases. Soft Computing techniques such as fuzzy logic, neural networks, genetic algorithms, rough sets, etc. aims to reveal the tolerance for imprecision and uncertainty for achieving tractability, robustness and low-cost solutions. Fuzzy Logic and Rough sets are suitable for handling different types of uncertainty. Neural networks provide good learning and generalization. Genetic algorithms provide efficient search algorithms for selecting a model, from mixed media data. Data mining refers to information extraction while soft computing is used for information processing. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Association rule mining (ARM) and Itemset mining focus on finding most frequent item sets and corresponding association rules, extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. This survey paper explores the usage of soft computing approaches in itemset utility mining

Artemether resistance in vitro is linked to mutations in PfATP6 that also interact with mutations in PfMDR1 in travellers returning with Plasmodium falciparum infections.

BACKGROUND: Monitoring resistance phenotypes for Plasmodium falciparum, using in vitro growth assays, and relating findings to parasite genotype has proved particularly challenging for the study of resistance to artemisinins. METHODS: Plasmodium falciparum isolates cultured from 28 returning travellers diagnosed with malaria were assessed for sensitivity to artemisinin, artemether, dihydroartemisinin and artesunate and findings related to mutations in pfatp6 and pfmdr1. RESULTS: Resistance to artemether in vitro was significantly associated with a pfatp6 haplotype encoding two amino acid substitutions (pfatp6 A623E and S769N; (mean IC50 (95% CI) values of 8.2 (5.7 - 10.7) for A623/S769 versus 623E/769 N 13.5 (9.8 - 17.3) nM with a mean increase of 65%; p = 0.012). Increased copy number of pfmdr1 was not itself associated with increased IC50 values for artemether, but when interactions between the pfatp6 haplotype and increased copy number of pfmdr1 were examined together, a highly significant association was noted with IC50 values for artemether (mean IC50 (95% CI) values of 8.7 (5.9 - 11.6) versus 16.3 (10.7 - 21.8) nM with a mean increase of 87%; p = 0.0068). Previously described SNPs in pfmdr1 are also associated with differences in sensitivity to some artemisinins. CONCLUSIONS: These findings were further explored in molecular modelling experiments that suggest mutations in pfatp6 are unlikely to affect differential binding of artemisinins at their proposed site, whereas there may be differences in such binding associated with mutations in pfmdr1. Implications for a hypothesis that artemisinin resistance may be exacerbated by interactions between PfATP6 and PfMDR1 and for epidemiological studies to monitor emerging resistance are discussed

Library Professionals’ Adoption of Cloud Computing Technologies: A Case Study on Kerala University Library, India

The purpose of this paper is to ascertain the awareness and use of cloud computing technologies among the library professionals in the Kerala University Library system, India. A survey was conducted using questionnaire among the 102 library professionals employed in the central and departmental libraries of the University of Kerala. The study revealed that 42.16% of the library professionals did not have much idea about cloud computing technology. Analysis showed that Facebook and Google Apps like Gmail, Google Doc etc are the cloud computing technologies used by majority of the respondents. It is also worth to note that the library professionals in Kerala University Library are using cloud computing technologies without being aware of doing so. Library professionals’ awareness of cloud service models is relatively very low. Web OPAC and Journal Discovery Service are the areas known to the respondents in applying cloud computing technologies in libraries. Out of 102 respondents, 14.71% of the library professionals in the University of Kerala have average skill in using these technologies. The findings of the study throw lights into the need of providing training for the library professionals in handling technology enriched library services to the users

PREDICTING THE VULNERABILITY AND RESILIENCE TO CARDIOVASCULAR AND NEUROENDOCRINE EFFECTS OF STRESS IN ADULT RATS THROUGH A NOVEL MACHINE LEARNING APPROACH

Chronic stress has been risk of cardiovascular disease and neuroendocrine illness in humans and animals. However, not all individuals are equally vulnerable to the negative effects of stress, and some may even exhibit resilience. Identifying biomarkers or other predictors of vulnerability and resilience could help to develop personalized prevention and treatment strategies. In this study, we aimed to predict vulnerability and resilience to stress-related health effects in adult rats using a novel machine learning approach. We exposed male rats to chronic stress or control conditions for six weeks and measured their cardiovascular and neuroendocrine responses at baseline and at the end of the stress exposure. Rats were considered vulnerable if they exhibited large growth in heart rate and reaction of blood pressure to stress, and resilient if they did not show significant changes in these parameters. We then applied a novel machine learning algorithm to identify patterns in the data that could predict vulnerability or resilience. In this case, we employed a combination methods for selecting features using Support Vector Machine and classification algorithm Principal component Analysis to identify the most important predictors of vulnerability and resilience. We also compared the performance of the machine learning approach with traditional statistical methods, such as logistic regression and discriminant analysis. Our results suggest that heart rate variability were among the most important predictor of vulnerability and resilience to stress-related health effects in rats. Specifically, rats with lower heart rate variability and higher cortisol levels at baseline were more likely to be vulnerable to stress. Conversely, rats with greater concentrations of anti-inflammatory cytokines increased risk of becoming resilient to stress. The machine learning approach was more accurate in predicting vulnerability and resilience than traditional statistical methods, with an overall accuracy of 89%, respectively. Our study provides new insights into the complex interplay between stress and health, and highlights the potential of machine learning to improve our understanding of this relationship. The identification of biomarkers and predictors of vulnerability and resilience could lead to the development of personalized approaches to stress management and prevention of stress-related health conditions

On validating a generic camera-based blink detection system for cognitive load assessment

Detecting the human operator\u27s cognitive state is paramount in settings wherein maintaining optimal workload is necessary for task performance. Blink rate is an established metric of cognitive load, with a higher blink frequency being observed under conditions of greater workload. Measuring blink rate requires the use of eye-trackers which limits the adoption of this metric in the real-world. The authors aim to investigate the effectiveness of using a generic camera-based system as a way to assess the user\u27s cognitive load during a computer task. Participants completed a mental task while sitting in front of a computer. Blink rate was recorded via both the generic camera-based system and a scientific-grade eye-tracker for validation purposes. Cognitive load was also assessed through the performance in a single stimulus detection task. The blink rate recorded via the generic camera-based approach did not differ from the one obtained through the eye-tracker. No meaningful changes in blink rate were however observed with increasing cognitive load. Results show the generic-camera based system may represent a more affordable, ubiquitous means for assessing cognitive workload during computer task. Future work should further investigate ways to increase its accuracy during the completion of more realistic tasks

Voice Feature Extraction for Gender and Emotion Recognition

Voice recognition plays a key role in spoken communication that helps to identify the emotions of a person that reflects in the voice. Gender classification through speech is a widely used Human Computer Interaction (HCI) as it is not easy to identify gender by computer. This led to the development of a model for “Voice feature extraction for Emotion and Gender Recognition”. The speech signal consists of semantic information, speaker information (gender, age, emotional state), accompanied by noise. Females and males have different voice characteristics due to their acoustical and perceptual differences along with a variety of emotions which convey their own unique perceptions. In order to explore this area, feature extraction requires pre- processing of data, which is necessary for increasing the accuracy. The proposed model follows steps such as data extraction, pre- processing using Voice Activity Detector (VAD), feature extraction using Mel-Frequency Cepstral Coefficient (MFCC), feature reduction by Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifier. The proposed combination of techniques produced better results which can be useful in the healthcare sector, virtual assistants, security purposes and other fields related to the Human Machine Interaction domain.&nbsp

PAPR Reduction and Sidelobe Suppression in Cognitive OFDM - A Survey

Cognitive radio (CR) is one of the key technology providing a new way to enhance the utilization of available spectrum effectively. The multicarrier modulation (MCM) technique which is widely used is Orthogonal Frequency Division Multiplexing (OFDM) system, is an excellent choice for high data rate application. The main two limitations of this technology is the high peak-to-average power ratio (PAPR) of transmission signal and large spectrum sidelobe. This article describes some of the important PAPR reduction techniques and sidelobe suppression techniques

WORK IN PROCESS OPTIMISATION THROUGH LEAN MANUFACTRING

ABSTRACT Today the global economy has caused a stronger competitive manufacturing environment in all kinds of business. Manufacturing industries face continuous pressure to reduce the price to remain in the market. It eventually results in manufactures need to reduce the profit margins in order to keep a share of the market. The objective of this paper to find the work in process for the optimal size using lean Techniques in a multiproduct single conveyor assembly line of a leading Two Wheeler Manufactures in south India. It is useful to map the dynamics of the supply chain focusing on how the demand information is passed from the final customer, back to the material suppliers and manufactures inside the company. So in this paper attempt has been made to find work in process and reduction of value in terms of Rupees from the current process to the proposed process. A mathematical model developed using general inventory cost model to quantify the Optimal Work In Process for the entire product range in Engine assembly line. Also numerical example is done to demonstrate the mathematical model with the available data. The mathematical results are very much encouraging and it calculated as 40 % reduction in work in process over the current work in process. KEY WORDS: WIP, optimal WIP, inventory cost method, change over time, multi product single conveyor assembly line