Economic Analysis of Menthol Mint Cultivation in Uttar Pradesh: A Case Study of Barabanki District

The present study has been carried out in the Barabanki district of Uttar Pradesh on economic analysis of menthol mint cultivation in the year 2010. The economics has been worked out by comparing costs and returns at different stages by the conventional method. The linear production function has been fitted to evaluate the resources-use efficiency in the production of menthol mint. The study has shown that the major portion of operational cost is shared by hired labour, interculture operations, distillation charges, irrigation and machine / tractor charge. The overall benefit-cost ratio has been found to be 2.55, which indicates a higher profit for farmers on less investment in mint cultivation. The independent variables like human labour, machinery, manures and fertilizer, irrigation charges and intercultural operations have shown a positive and significant impact on the returns of mentha crop in the study area. The major problems faced by the farmers are high input cost, erratic supply of electricity, lack of adequate information, infrastructural facilities, regulated markets and energy-efficient distillation units.Menthol mint, Medicinal and aromatic plants, Mentha crop, Barabanki district, Economic analysis, Agricultural and Food Policy, Q 12, Q 18,

Hydrometallurgy

Hydrometallurgy, which involves the use of aqueous solutions for the recovery of metals from ores, concentrates, and recycled or residual material, plays an integral role in the multi-billion dollar minerals processing industry. There are numerous hydrometallurgical process technologies used for recovering metals, such as: agglomeration; leaching; solvent extraction/ion exchange; metal recovery; and remediation of tailings/waste. Modern hydrometallurgical routes to extract metals from their ores are faced with a number of issues related to both the chemistry and engineering aspects of the processes involved. These issues include declining ore grade, variations in mineralogy across the deposits and geo-metallurgical locations of the ore site; which would influence the hydrometallurgical route chosen. The development of technologies to improve energy efficiency, water/resources consumption and waste remediation across the circuit is also an important factor to be considered. Therefore, there is an increasing need to develop novel solutions to these existing problems, to implement environmentally sustainable practices in the recovery of these valuable metals. Papers on recent advances, and review articles, particularly in regard to fundamental chemistry and the development of novel techniques and technologies in commercial processing of mineral commodities from their ores, are included in this Special Monograph on "Hydrometallurgy"

Maternal inheritance of chloroplast DNA in Coffea arabica hybrids

Intergenic spacers of chloroplast DNA (cpDNA) are very useful in phylogenetic and population genetic studies of plant species. Their integration in phylogenetic analysis leads to a deep understanding of species relationships. Mode of chloroplast DNA inheritance was investigated in the genus Coffea arabica (Rubiaceae) by polymerase chain reaction (PCR) amplification of cpDNA fragments using universal primer pair rrn23 – trnR (ACG) region of chloroplast genome. A total of 30 F1 plants from five different C. arabica parent varieties were examined. Two crosses involving C. arabica var. Sln.10 and C. arabica var. Devamachy, and C. arabica var.S.881 and another cross involving C. arabica var. Agaro with C. arabica var. Tafarikela were analyzed. Length polymorphism was observed in all three hybrids at rrn23 – trnR (ACG) region of cpDNA. In each case, it was the maternal cpDNA marker that appeared in the F1 individuals. Further it was observed that the length polymorphism  observed within parental plants led to the identification of five different banding patterns at rrn23 – trnR (ACG) region of coffee cpDNA. The possible reasons for the observed differences are discussed

Transformation Potential of Cloud Computing - Understanding Strategic Value Creation from Customer and Vendor Perspectives.

While Cloud Computing is evolving as a major information technology phenomenon by redefining how IT capabilities are generated and consumed, the business value of this emerging model of IT capabilities delivery is anecdotal. Limited empirical research exists to my knowledge on what and how business value is created from these technologies. My dissertation devises three empirical studies to systematically investigate the business value of cloud computing technologies from the customer and vendor perspectives. In particular, I examine the transformation potential of these technologies in delivering strategic benefits that transcend beyond mere cost advantages often cited in practitioner literature. From the customer perspective, I investigate the strategic benefits these technologies create towards organizational and individual role effectiveness. In one study, I examine at the organizational level if adopting these technologies can be associated with the IT-enabled business innovation of the firms. At the individual role level investigated in another study, I examine the association between cloud computing adoption and the involvement of Chief Information Officers in strategic opportunities related to innovation and new product development. From the vendor perspective, I examine in my third study, the implications of cloud computing architectures for the vendor organizations. I attempt to understand what changes in the technical and organizational functions are needed in the vendor organizations to reorient themselves to create the expected business value and succeed in the cloud computing market. Through these three empirical studies, my dissertation is a systematic attempt to shed light on the strategic business benefits of cloud computing and the enablers of value creation in the cloud-based technology model.PhDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110393/1/sureshms_1.pd

Asymptomatic Bacteriuria in Diabetic Adults

Introduction: Urinary Tract Infection (UTI) is a well- known complication of Diabetes Mellitus (DM). Its spectrum ranges from Asymptomatic Bacteriuria (ABU) to acute pyelonephritis. Many studies have delineated an increased prevalence of ABU in DM whereas to the same degree other studies have come to naught showing insignificant association. Hence, this study was drafted to evaluate the presence of ABU among diabetics and assess various risk factors. Methods: Total of 116 diabetic adults without symptoms of UTI attending medical out-patient department, Manipal Teaching Hospital were enrolled by detailed clinical history, examination and laboratorial examination as per standard set of questionnaire from February 2013 to May 2014. Data were analyzed by SPSS (17.0). Results: The rate of ABU in diabetic adults was 10.3% and was significantly associated with duration of DM, fasting blood glucose level and poor glycaemic control. Escherichia coli was the most frequently isolated pathogen which was sensitive to Nitrofurantoin and Imipenem. Conclusion: Being asymptomatic, diabetics fail to recognise ABU, however, ABU is preponderant in DM and is linked mainly with duration of DM and poor glycaemic control. Hence screening for ABU is imperative in diabetic adults if above mentioned risk factors are present

An Effective Deep Learning Based Multi-Class Classification of DoS and DDoS Attack Detection

In the past few years, cybersecurity is becoming very important due to the rise in internet users. The internet attacks such as Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks severely harm a website or server and make them unavailable to other users. Network Monitoring and control systems have found it challenging to identify the many classes of DoS and DDoS attacks since each operates uniquely. Hence a powerful technique is required for attack detection. Traditional machine learning techniques are inefficient in handling extensive network data and cannot extract high-level features for attack detection. Therefore, an effective deep learning-based intrusion detection system is developed in this paper for DoS and DDoS attack classification. This model includes various phases and starts with the Deep Convolutional Generative Adversarial Networks (DCGAN) based technique to address the class imbalance issue in the dataset. Then a deep learning algorithm based on ResNet-50 extracts the critical features for each class in the dataset. After that, an optimized AlexNet-based classifier is implemented for detecting the attacks separately, and the essential parameters of the classifier are optimized using the Atom search optimization algorithm. The proposed approach was evaluated on benchmark datasets, CCIDS2019 and UNSW-NB15, using key classification metrics and achieved 99.37% accuracy for the UNSW-NB15 dataset and 99.33% for the CICIDS2019 dataset. The investigational results demonstrate that the suggested approach performs superior to other competitive techniques in identifying DoS and DDoS attacks

Promoting the adoption of improved ICM technologies in chickpea by poor farmers in Nepal

Chickpea is an important source of protein in Nepal for both poor rural and urban families. Yields of legumes including chickpea have decreased in recent years, primarily due to disease and insect pest problems, and the reluctance of farmers to invest time and money in a crop which increasingly fails. This project aimed to improve, validate and evaluate a complete integrated crop management (ICM) strategy for chickpea on farms; to persuade farmers to adopt the strategy through workshops and farm trials in some of the poorest areas of Nepal; and to promote the strategy widely to the press. and other stakeholders. The improved seed and technologies offered were in high demand by farmers, and the project produced evidence to show that the outputs (increased yield of a cash crop) have been translated into significant impacts on livelihood, including improved housing, debt repayment, education costs, clothes and healthcare. The value of the improved seed has been impressive and will benefit farmers in the longer term. An innovative promotional strategy promoted the findings to policymakers as well as farmers, and used the popular media in Nepal and the UK to publicise the wor

Increased fetal adiposity prior to diagnosis of gestational diabetes in South Asians : more evidence for the ‘thin–fat’ baby

Aims/hypothesis Gestational diabetes mellitus (GDM) is associated with an increased future risk of obesity in the offspring. Increased adiposity has been observed in the newborns of women with GDM. Our aim was to examine early fetal adiposity in women with GDM. Methods Obstetric and sonographic data was collated for 153 women with GDM and 178 controls from a single centre in Chennai, India. Fetal head circumference (HC), abdominal circumference (AC), femur length (FL) and biparietal diameter (BPD) were recorded at 11, 20 and 32 weeks. Anterior abdominal wall thickness (AAWT) as a marker of abdominal adiposity at 20 and 32 weeks was compared between groups. Adjustments were made for maternal age, BMI, parity, gestational weight gain, fetal sex and gestational age. Results Fetuses of women with GDM had significantly higher AAWT at 20 weeks (β 0.26 [95% CI 0.15, 0.37] mm, p < 0.0001) despite lower measures of HC, FL, BPD and AC. AAWT remained higher in the fetuses of women with GDM at 32 weeks (β 0.48 [0.30, 0.65] mm, p < 0.0001) despite similar measures for HC, FL, BPD and AC between groups. Both groups had similar birthweights at term. There was an independent relationship between fasting plasma glucose levels and AAWT after adjustment as described above. Conclusions/interpretation A ‘thin but fat’ phenotype signifying a disproportionate increase in adiposity despite smaller or similar lean body mass was observed in the fetuses of mothers with GDM, even at 20 weeks, thus pre-dating the biochemical diagnosis of GDM. Increased AAWT may serve as an early marker of GDM