State incapacity by design: unused grants, poverty and electoral success in Bihar

This thesis offers a perspective on why majority-poor democracies might fail to pursue pro-poor policies. In particular, it discusses why in Bihar, the Rashtriya Janata Dal (RJD) party led by Lalu Prasad Yadav, which claimed to represent the poor and under-privileged, did not claim and spend large amounts of centre–state fiscal transfers that could have reduced poverty, provided employment and benefitted core supporters. Despite this failure, the RJD and Yadav enjoyed repeated electoral success between 1990 and 2005, in a context of credible elections and a majority of poor voters. I have called this combination of events the ‘Bihar paradox’. I explore this paradox by: 1. Creating two panel data sets on fiscal transfers in the form of Centrally Sponsored Schemes and State Plan Allocations made from the Government of India to sixteen main states over an eight-year period from 1997-98 to 2004-05. 2. Using the panel data sets to show that, during this period: a) Poor states in India claimed and spent less of the centre–state fiscal transfers available to them than wealthier states b) Relative to other states, the Government of Bihar claimed and spent less fiscal transfers than expected of a state at its level of poverty. 3. Explaining how Yadav’s electoral strategy contributed to this under-claiming and under-spending. For Yadav, the political imperative was to marginalize the upper castes and provide selective benefits to key supporters. This led to large numbers of public sector vacancies which eroded state administrative capacity in all but a few ‘pockets of productivity’, which in turn led to poor results for general development outcomes. The Bihar story is relevant to areas of research variously labelled as ‘state-building’, ‘capacity development’ and ‘public sector reform’. It is another warning about how easy it is to foster pessimism by attributing governance problems in poor countries to deeply embedded historical or cultural factors, when they may have more immediate, political and tractable causes

Comparison of Students’ Performance Using Web-Based and Paper-Based Homework in an Undergraduate Statistics Course

Our institution is heavily invested in improving the learning outcomes of our students, especially in core courses like statistics which is taken by majority of freshmen students. As such, we have been reviewing our existing pedagogy and exploring novel ways to improve students experience in statistics course.  In this study we compare web-based vs paper-based traditional homework models and their impact on students’ performance in an undergraduate statistics course. In web-based model, students used MyStatLab to complete the homework assignments along with additional resources offered by this platform. On the other hand, in paper-based model students used traditional approach in completing the homework and was manually graded by the instructor.  Our analysis indicated that there is no statistically significant difference in students’ final course grades between the two modalities. Additionally, we conducted a chi-square test of independence and found no statistically significant relationship between students’ final course grades and homework models. However, our analysis concluded that student performance and perceptions improved with online homework model compared to the traditional model. Keywords: Web-based online homework, Traditional homework, Student performance, Undergraduate Statistics DOI: 10.7176/JEP/13-27-09 Publication date:September 30th 202

Efficacy of Mastery-Based Adaptive Technology in Introductory Quantitative Reasoning Course

In this study, we explored the efficacy of using Hawkes Learning, a mastery-based adaptive technology system in a freshmen level Quantitative Reasoning course at a four-year private university. Students used this competency based adaptive platform to master the content by learning the material at their own pace, then practicing the problems and finally getting certified on their homework assignments. Each topic of this system offered three intuitive modes to mastery: Learn, Practice, and Certify. We analyzed students’ performance and perceptions with this system and compared it with the previous traditional model. Our study indicated that there is no statistically significant difference between students’ performance but their perceptions towards learning have improved significantly when they used this technology. We found that most students were able to succeed in QR course with this technology and it enabled them to become better independent learners. Keywords: Mastery-Based Learning, Adaptive Technology, Digital Learning, Quantitative Reasoning, Introductory Mathematics Course DOI: 10.7176/JEP/10-24-02 Publication date: August 31st 201

ROLE OF ANTIDEPRESSANT ON THE GLYCAEMIC CONTROL OF UNCONTROLLED TYPE 2 DIABETES MELLITUS PATIENTS

Background: Depression incidence is higher in diabetic patients when compared to the non-diabetic individuals and there exist a two-directional relationship between depression and the development of type 2 diabetes mellitus. Objectives: This study aimed to estimate the frequency of depression and the effect of antidepressant on glycemic control in type 2 diabetes mellitus patients. Methods: This prospective interventional study was conducted in type 2 diabetes mellitus patients with a sample size of 100. These patients were diagnosed with depression using WHO-ICD10 criteria. All study patients had uncontrolled blood glucose levels and were on an optimized maximal dose of combination oral hypoglycemic agents with stable glycoregulation (HbA1c 8.4 ±0.5) were taken up for the intervention with antidepressant. These patients were started on with antidepressant after enrollment and followed up for fasting blood sugar (FBS), post-prandial blood sugar (PPBS), and HbA1c at the end of 3 months and 6 months. And also Hamilton depression rating scale scores were estimated at the beginning of the study and at the end of 6 months. Results: The frequency of depression among the type 2 diabetes mellitus patients was found to be 42%. There were reduction of mean FBS levels from baseline value of 177 mg/dl to follow-up value of 160 mg/dl (p<0.001), mean PPBS levels from 251.16 mg/dl to 217.84 mg/dl (p<0.001), and mean HbA1c dropped from 8.41 to 7.57 (p<0.001) after the treatment with antidepressant. Conclusion: Our study concluded that patient started on antidepressant showed a reduction in the blood sugar levels and HbA1c levels from their baseline values, which was clinically and statistically significant

Food spectrum and dietary preferences of the Indian anchovy Stolephorus indicus (van Hasselt, 1823) from Thiruvananthapuram coast, Kerala

The food preferences of the Indian anchovy Stolephorus indicus (van Hasselt, 1823) along the Thiruvananthapuram coast of Kerala was studied for a period of one year from June 2013 to May 2014, dividing the entire period into three seasons as pre-monsoon, monsoon and post-monsoon. A total of 141 samples were collected and the gut contents were analysed. The principal food item was the crustaceans which included copepods, lucifers, mysids, Acetes and amphipods. The other preferred prey items were molluscs (bivalves and gastropods), small fishes, tintinnids and dinoflagellates. The gastrosomatic and stomach fullness indices revealed almost uniform feeding preferences with copepods being the preferred food item throughout the three seasons. Analysis of variance showed significant (p0.05) seasonal variation was observed in the gut contents of S. indicus. Analyses of the different prey indices [prey diversity index (H), niche width indices (B) and prey evenness indices (e)] of S. indicus for the three seasons indicated an almost uniform distribution of prey species throughout the study period which directly indicate the abundance of the prey items and indirectly indicate a stable potential fishery and ecosystem

E-governance, accountability, and leakage in public programs : experimental evidence from a financial management reform in India

Can e-governance reforms improve government policy? By making information available on a real-time basis, information technologies may reduce the theft of public funds. We analyze a large field experiment and the nationwide scale-up of a reform to India's workfare program. Advance payments were replaced by "just- in-time" payments, triggered by e-invoicing, making it easier to detect misreporting. Leakages went down: program expenditures dropped by 24%, while employment slightly increased; there were fewer fake households in the official database; program officials' personal wealth fell by 10%. However, payment delays increased. The nationwide scale-up resulted in a persistent 19% reduction in program expenditure

Physiological response of cocoa (Theobroma cacao L. ) genotypes to drought

Drought is one of the major environmental stresses affecting crop productivity worldwide. Climate change is expected to result in a rise in the number and intensity of drought events in the coming decades, so climate-resilient crops that can withstand this stress are in high demand. There are few genotypes in cocoa where it can tolerate water deficit conditions. The objective of the current investigation was to evaluate the effect of drought stress on the photosynthetic and physiological parameters of six cocoa genotypes (Theobroma cacao L.) with two irrigation regimes (100% field capacity and 40% field capacity) under greenhouse conditions at Cocoa Research Station, Kerala Agricultural University, Thrissur. The effect of water deficit conditions on gas exchange and physiological parameters such as relative water content, membrane stability index, chlorophyll stability index, and chlorophyll content were evaluated. Drought stress conditions resulted in reduced photosynthetic rate, relative water content, chlorophyll content, chlorophyll stability and membrane stability. All genotypes revealed significant differences for most parameters with two irrigation regimes. Among the cocoa genotypes, P.IV 19.9, which is classified as a highly tolerant genotype, recorded better results for all the parameters studied under water deficit conditions at 40 per cent FC. The findings of this study support the classification of these genotypes as highly tolerant, tolerant, and susceptible. These parameters may be used as the most promising indicators to screen for drought tolerance in cocoa. The results of the study revealed that photosynthetic and physiological parameters have a significant role in imparting drought stress tolerance to cocoa. Furthermore, these selected drought-tolerant genotypes can be used in future crop improvement programmes in cocoa

AutoML accurately predicts endovascular mechanical thrombectomy in acute large vessel ischemic stroke

Background and objectiveAutomated machine learning or autoML has been widely deployed in various industries. However, their adoption in healthcare, especially in clinical settings is constrained due to a lack of clear understanding and explainability. The aim of this study is to utilize autoML for the prediction of functional outcomes in patients who underwent mechanical thrombectomy and compare it with traditional ML models with a focus on the explainability of the trained models.MethodsA total of 156 patients of acute ischemic stroke with Large Vessel Occlusion (LVO) who underwent mechanical thrombectomy within 24 h of stroke onset were included in the study. A total of 34 treatment variables including clinical, demographic, imaging, and procedure-related data were extracted. Various conventional machine learning models such as decision tree classifier, logistic regression, random forest, kNN, and SVM as well as various autoML models such as AutoGluon, MLJAR, Auto-Sklearn, TPOT, and H2O were used to predict the modified Rankin score (mRS) at the time of patient discharge and 3 months follow-up. The sensitivity, specificity, accuracy, and AUC for traditional ML and autoML models were compared.ResultsThe autoML models outperformed the traditional ML models. For the prediction of mRS at discharge, the highest testing accuracy obtained by traditional ML models for the decision tree classifier was 74.11%, whereas for autoML which was obtained through AutoGluon, it showed an accuracy of 88.23%. Similarly, for mRS at 3 months, the highest testing accuracy of traditional ML was that of the SVM classifier at 76.5%, whereas that of autoML was 85.18% obtained through MLJAR. The 24-h ASPECTS score was the most important predictor for mRS at discharge whereas for prediction of mRS at 3 months, the most important factor was mRS at discharge.ConclusionAutomated machine learning models based on multiple treatment variables can predict the functional outcome in patients more accurately than traditional ML models. The ease of clinical coding and deployment can assist clinicians in the critical decision-making process. We have developed a demo application which can be accessed at https://mrs-score-calculator.onrender.com/