EXPECTATIONS OF CONSUMERS OF STORE BRAND APPARELS IN CHENNAI CITY OF INDIA

Abstract    The growth of store brands in India presents an interesting opportunity for the retailer to understand the motivations of consumers behind choice of store brands. Since, Chennai is not a saturated market, and with an expected growth of 10-12% over the next 10 years. Chennai is under-branded and under penetrated in many categories of apparels. Clearly, Chennai market has scope for more brands and all brands can co exist. Hence, among the four major metropolises in India, The data and information have been collected from 500 consumers of store brand apparels in Chennai city by adopting multi stage random sampling technique through pre-tested, structured interview schedule through direct interview method pertains to the year 2009-2010. The results shows that more than one third of consumers belong to the middle age group and nearly two third of them are married. About of half of the consumers are graduates and majority of consumers belong to middle income group. The results show that more than half of the consumers are employed in private sector and nearly two third of them visit the apparel store once in a month followed by every two months. The spouse and children are highly influencing the purchasing decision and most of them suggest the price of apparel store brands and nearly half of them purchase store brand apparels worth of Rs..2001-3000 per visit. The discriminant analysis indicates that lift/elevator, price tag, lighting and accessories discriminate best among age groups while comfortable, courtesy, convenience and quick delivery discriminate best among income groups. Hence, it is suggested that styling should be specific and sensitive to the large-size male consumers taking into account fashion ability and uniqueness. To remain competitive, apparel stores must continue low pricing and weekly sale advertisements. In addition, stores must continue to promote their store labels and brands as well as focus on small niche markets. Without a combination of low price and high quality, store brands will not succeed. Low price store brands end up eliminating price competition, which intensifies consumer price sensitivity, satisfaction and loyalty.   Keywords: Store Brands, Consumers Expectations, Discriminant Analysi

Expanding the Scope of Corporate Social Responsibility (CSR): Strategizing Skill Development in the Indian Scenario

India is transitioning demographically with a large population of youngsters. To harness this population trend into a „demographic dividend,‟ it is essential to enhance the skill level of our youth. The Government of India (GoI) has taken many proactive measures in this regard. „Skill India Campaign‟ is one such measure. Though India‟s corporate sector has also been contributing to skill training through its Corporate Social Responsibility (CSR) initiative, the efforts have been few and far between. The first part of this paper explores the Skill scenario of India, and the role played by both Public and Private sector to address the current skill gap. The second part of the paper suggests a possible solution to address the „skill gap‟ through a proactive Public-private partnership (PPP) by implementing a remodelled CSR strategy. Government and corporate sector can work together in the skill training arena through CSR and make it a mutually beneficial, sustainable activity to develop India into a “skill capital” of the World. The potential advantages of such a partnership for each player involved are also explored in detail in this part

Design and Realization of an Unmanned Aerial Rotorcraft Vehicle Using Pressurized Inflatable Structure

Unmanned aerial rotorcraft vehicles have many military, commercial and civil applications. There is a necessity to advance the performance on several ranges of rotorcraft for using these vehicles successfully in the expanded future roles. A lower flight time, noise disturbance and safety issues remain the key obstacles in increasing the efficiency of the rotorcraft for various applications. This work presents the design and realization of a rotorcraft using pressurized inflatable structure filled with lighter than air gas such as helium or hydrogen to provide lift assistance for the vehicle. Two iterative design procedures were developed for designing the vehicle. One is based on the net weight of the vehicle and the other based on the diameter of the pressurized structure. Fabrication of a design based on the diameter of the pressurized structure is analysed and evaluated. Gross static lift, the correlation between the size of the inflatable structure and lift force produced, lifting gas properties in the flight range, stress on the structure, and the maximum achievable altitude is also discussed. The vehicle possesses the potential to overcome some inherent limitations of the current unmanned aerial rotorcraft vehicles. This work holds an excellent prospect for future research and more isolated development in all the applications this particular system can be employed

Understanding Below-replacement Fertility in Kerala, India

Kerala is well-known globally for the unprecedented fertility transition in the Indian subcontinent towards the end of the last century. The state has already reached below-replacement fertility level in the 1990s while the rest of India was experiencing high or mid-level fertility. With this backdrop, an attempt was made in this paper (a) to explore the plausible factors associated with sub-replacement fertility and consequent population momentum in Kerala and (b) to trace their socioeconomic and health policy implications. The underlying factors that led to the fertility transition was explored and discussed in some detail. An enhanced level of human development achieved during the last quarter of the 20th century, mainly through developments in social and health sectors, is likely to be the main contributor. Unlike other states in India, there were historical factors as well that functioned as a catalyst for this, such as widespread education and women's empowerment. As an inevitable demographic impact, population growth due to momentum is expected to be very strong in Kerala with an age-structural transition favouring the old. The so-called ‘demographic dividend’ invoked by the increase of labour-force derived from the youth bulge in the age-structure is being lost in the state due to very limited capital investments and political will. Again, as a direct consequence of population growth, population density in Kerala will take a staggering level of 1,101 persons per sq km in 2026. The ill effects of environmental deterioration and consequent changes in morbidity patterns will have to be dealt with seriously. The very foundations of health policy needs revamping in the light of demographic changes associated with sub-replacement fertility. The tempo of population-ageing is very high in Kerala. The proportion of population aged 60+ years is likely to be 20% in 2026 whereas it will be around 12% only in India. The current level of social and health infrastructure in the state may not be sufficient to cope with the emerging demands of population-ageing since the financial and morbidity burdens of the elderly are already quite high. To conclude, Kerala portrays a typical case of the vagaries of the onset of sub-replacement fertility level in the absence of reasonable structural changes in the economic and health fronts

First Day Predictors of Requirement of Mechanical Ventilation in COPD Patients with Acute Exacerbation

BACKGROUND: Since burden of COPD vastly outnumbers the critical care facilities in India, identification on the day of admission itself of those patients with acute exacerbations of COPD will allow the physician to properly utilize these precious resources for deserving patients. AIMS AND OBJECTIVES: To identify the first day predictors of requirement of mechanical ventilation in COPD patients with acute exacerbation METHODS: All the patients will undergo detailed clinical evaluation, appropriate investigations. The demographic profile collected by questionnaire at the time of admission will include age, sex, smoking status. Patient vitals including heart rate, blood pressure and respiratory rate will be recorded. Premorbid functional status according to the modified Menzies criteria will be calculated from the patient or relatives if the patient is unable to provide the details. Arterial blood gas analysis, liver function tests, renal function tests, serum electrolytes will be done routinely for the patients. Acute Physiology and Chronic Health Evaluation II (APACHE II) score will be calculated for each patient from the following data (Age, Temperature, Mean Arterial Pressure, Heart Rate, Respiratory Rate, FiO2, Arterial pH, Serum HCO3, Serum sodium, Serum Potassium, Serum Creatinine, Packed Cell Volume, WBC count, Glasgow Coma Scale). All patients were treated with the same initial measures and intubated only if they fit into indications for mechanical ventilation. Finally the parameters were compared between those patients treated conservatively and those who eventually needed mechanical ventilation. RESULTS: In our study the association between duration of cigarette smoking in pack years and the need for mechanical ventilation was found to be significant, hence COPD patients with long duration of smoking are more likely to require mechanical ventilation during acute exacerbation. The association between low arterial blood pH (< 7.2) and the need for mechanical ventilation was found to be significant, hence COPD patients with more acidosis are more likely to require mechanical ventilation in an acute exacerbation. The association between high PaCO2 (> 60mm Hg) on the day of admission and the need for mechanical ventilation was found to be significant, hence COPD patients with higher PaCO2 are more likely to require mechanical ventilation in an acute exacerbation. Association between low GCS (<13) and the need for mechanical ventilation was significant, hence COPD patients with altered sensorium are likely to need mechanical ventilation in an acute exacerbation. The association between high APACHE II score (>15) and the need for mechanical ventilation was significant, hence COPD patients with high APACHE II score on the day of admission are more likely to require mechanical ventilation. The association between low serum albumin (<3g/dl) and the need for mechanical ventilation was found significant, hence COPD patients with less serum albumin are more likely to require mechanical ventilation in an acute exacerbation. Association between premorbid functional status (Grade III and IV) as measured by modified Menzies scale and the need for mechanical ventilation was significant, hence COPD patients with higher premorbid score are more likely to require mechanical ventilation in an acute exacerbation. CONCLUSION: Hence, our study has found that duration of smoking in pack years, first day values of arterial blood pH, PaCO2, Glasgow Coma Scale, APACHE II score, serum albumin and premorbid functional status can be used to predict the need for mechanical ventilation in COPD patients with acute exacerbation

DENS-ECG: A Deep Learning Approach for ECG Signal Delineation

Objectives: With the technological advancements in the field of tele-health monitoring, it is now possible to gather huge amounts of electro-physiological signals such as electrocardiogram (ECG). It is therefore necessary to develop models/algorithms that are capable of analysing these massive amounts of data in real-time. This paper proposes a deep learning model for real-time segmentation of heartbeats. Methods: The proposed algorithm, named as the DENS-ECG algorithm, combines convolutional neural network (CNN) and long short-term memory (LSTM) model to detect onset, peak, and offset of different heartbeat waveforms such as the P-wave, QRS complex, T-wave, and No wave (NW). Using ECG as the inputs, the model learns to extract high level features through the training process, which, unlike other classical machine learning based methods, eliminates the feature engineering step. Results: The proposed DENS-ECG model was trained and validated on a dataset with 105 ECGs of length 15 minutes each and achieved an average sensitivity and precision of 97.95% and 95.68%, respectively, using a 5-fold cross validation. Additionally, the model was evaluated on an unseen dataset to examine its robustness in QRS detection, which resulted in a sensitivity of 99.61% and precision of 99.52%. Conclusion: The empirical results show the flexibility and accuracy of the combined CNN-LSTM model for ECG signal delineation. Significance: This paper proposes an efficient and easy to use approach using deep learning for heartbeat segmentation, which could potentially be used in real-time tele-health monitoring systems