Private Coaching Centres in India: A Document Analysis of JEE-Advanced Preparation Centres on the Lives of Students in Kota

Gone are the days when tuition or coaching classes were meant for academically weak students. With the Economic Reforms of 1991 (ER91), the Indian education system went through a series of changes, the most prominent being the growth of private educational institutions across the country. This led to creation of a billion-dollar coaching industry in India. Due to the ease of setting up private institutions and the absence of any regulatory body to vouch for them, the private institutions became immensely commercialized. The purpose of this study is to investigate the organizational structure of such coaching centres and analyze the various aspects of the organizational framework within which they operate. As the coaching industry is widespread, the study focuses on one of the popular coaching sectors that prepares aspiring engineers for the national level JEE- Advanced examination in a small town (Kota, in the northern state of Rajasthan) that has garnered a significant reputation as the coaching capital of the country

Effectiveness of Surface Treatment Techniques for Composite Bonding with Different Contamination Levels

Various surface treatment techniques have been developed to promote adhesive bond performance for composite structural components in aerospace applications. The condition of the pre-bond surface is critical to achieving desirable bond quality. Contamination on bonding surfaces is well recognized as a major threat to ultimate bond performance. Variation in contamination level has brought additional challenges into manufacturing process control. High fidelity surface treatment techniques are required for effective removal of contaminants over a wide range of contamination levels. In this study, a common contaminant, i.e. silicone mold release, was introduced to pre-bond composite surfaces with different concentrations. Plasma and laser surface treatment techniques were performed and their effectiveness in restoring and enhancing desirable bond quality was investigated. Surface characterization techniques, including water contact angle goniometry, Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy, were conducted to assess the condition of contaminated surfaces and the improvement induced by plasma and laser surface treatments. Failure modes from a customized double cantilever beam test were investigated before and after surface treatments. Fundamental mechanisms of plasma and laser surface treatments on the composite bonding surfaces were also investigated

Compiler and system for resilient distributed heterogeneous graph analytics

Graph analytics systems are used in a wide variety of applications including health care, electronic circuit design, machine learning, and cybersecurity. Graph analytics systems must handle very large graphs such as the Facebook friends graph, which has more than a billion nodes and 200 billion edges. Since machines have limited main memory, distributed-memory clusters with sufficient memory and computation power are required for processing of these graphs. In distributed graph analytics, the graph is partitioned among the machines in a cluster, and communication between partitions is implemented using a substrate like MPI. However, programming distributed-memory systems are not easy and the recent trend towards the processor heterogeneity has added to this complexity. To simplify the programming of graph applications on such platforms, this dissertation first presents a compiler called Abelian that translates shared-memory descriptions of graph algorithms written in the Galois programming model into efficient code for distributed-memory platforms with heterogeneous processors. An important runtime parameter to the compiler-generated distributed code is the partitioning policy. We present an experimental study of partitioning strategies for distributed work-efficient graph analytics applications on different CPU architecture clusters at large scale (up to 256 machines). Based on the study we present a simple rule of thumb to select among myriad policies. Another challenge of distributed graph analytics that we address in this dissertation is to deal with machine fail-stop failures, which is an important concern especially for long-running graph analytics applications on large clusters. We present a novel communication and synchronization substrate called Phoenix that leverages the algorithmic properties of graph analytics applications to recover from faults with zero overheads during fault-free execution and show that Phoenix is 24x faster than previous state-of-the-art systems. In this dissertation, we also look at the new opportunities for graph analytics on massive datasets brought by a new kind of byte-addressable memory technology with higher density and lower cost than DRAM such as intel Optane DC Persistent Memory. This enables the design of affordable systems that support up to 6TB of randomly accessible memory. In this dissertation, we present key runtime and algorithmic principles to consider when performing graph analytics on massive datasets on Optane DC Persistent Memory as well as highlight ideas that apply to graph analytics on all large-memory platforms. Finally, we show that our distributed graph analytics infrastructure can be used for a new domain of applications, in particular, embedding algorithms such as Word2Vec. Word2Vec trains the vector representations of words (also known as word embeddings) on large text corpus and resulting vector embeddings have been shown to capture semantic and syntactic relationships among words. Other examples include Node2Vec, Code2Vec, Sequence2Vec, etc (collectively known as Any2Vec) with a wide variety of uses. We formulate the training of such applications as a graph problem and present GraphAny2Vec, a distributed Any2Vec training framework that leverages the state-of-the-art distributed heterogeneous graph analytics infrastructure developed in this dissertation to scale Any2Vec training to large distributed clusters. GraphAny2Vec also demonstrates a novel way of combining model gradients during training, which allows it to scale without losing accuracyComputer Science

Motion Detection in Low Resolution Grayscale Videos Using Fast Normalized Cross Correrelation on GP-GPU

Motion estimation (ME) has been widely used in many computer vision applications, such as object tracking, object detection, pattern recognition and video compression. The most popular block based similarity measures are the sum of absolute differences (SAD), the sum of squared differences (SSD) and the normalized cross correlation (NCC). Similarity measure obtained using NCC is more robust under varying illumination changes as compared to SAD and SSD. However NCC is computationally expensive and application of NCC using full or exhaustive search method further increases required computational time. Relatively efficient way of calculating the NCC is to pre-compute sum-tables to perform the normalization referred to as fast NCC (FCC). In this paper we propose real time implementation of full search FCC algorithm applied to gray scale videos using NVIDIA’s Compute Unified Device Architecture (CUDA). We present fine-grained optimization techniques for fully exploiting computational capacity of CUDA. Novel parallelization strategies adopted for extracting data parallelism substantially reduce computational time of exhaustive FCC. We show that by efficient utilization of global, shared and texture memories available on CUDA, we can obtain the speedup of the order of 10x as compared to the sequential implementation of FCC

Glass Stability and Effect of Heat-Treatment Duration on Chemical Interaction between Calcium Lanthanum Borosilicate Glass Sealant and Electrolytes

The diffusion couples of glass (30CaO-40SiO 2 -20B 2 O 3 -10La 2 O 3 ) with high (Yttria stabilized zirconia) and low (titanium doped bismuth vanadate) temperature electrolytes have been prepared and heat-treated at 850 • C and 800 • C respectively for 5, 100 and 750 h to study the effect of heat-treatment duration on chemical reactivity and interface formation. The prepared diffusion couples have been characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray dot mapping and electron probe microanalysis (EPMA). The XRD studies for coupled glass indicated different crystalline phase formation after prolonged heat-treatment duration of 750 h. Microstructural analysis of diffusion couples has not shown the presence of crystalline SiO 2 (cristobalite) at the interface. In the present study, the efforts have been made to understand the interfacial reaction by considering different theoretical parameters associated with glass seal. Different type of materials have been chosen as sealing materials such as mica, ceramic fibers, brazing and glass/glass-ceramics for solid oxide fuel cells. 1-5 Seals are exposed to high temperature under different atmosphere and pressure. 6-10 Substantial work has been done in the area of glass and glass-ceramics, as they are ideal sealing candidates because of their unique properties such as high corrosion and oxidation resistance as well as easy processing. However, none of the glass seal developed so far exhibit good sealing properties along with long life. Above all, modification in the properties of glasses can be tailored using suitable components to achieve good and highly compatible seal. have been investigated as SOFC sealing materials. For implementing the glass seals to SOFC, seal properties such as flow, adhesion, CTE and chemical reactivity between glass and its surrounding components play a crucial role in determining long-term reliability as well as structural integrity. The initial composition of glasses play very important role to tailor their properties. For instance, CaO reduces glass transition temperature (T g ) and glass crystallization temperature (T c ), limits corrosion along with raising the CTE. La 2 O 3 is investigated as a viscosity modifier and long-term coefficient of thermal expansion (CTE) stabilizer whereas B 2 O 3 is a low temperature glass former and a good wetting agent. 35-37 TBV exhibits excellent conductivity along with thermal stability at 800 • C. Experimental The composition of CL glass is 30CaO-40SiO 2 -20B 2 O 3 -10La 2 O 3 (in mol%). The preparation details of the CL glass chosen for the present study are given elsewhere. 33 A high-purity YSZ powder (TZ-8YS, Tosoh) was used for making the pellets of dimensions 10 mm × 10 mm × 3 mm sintered at 1450 • C for 12 h in high resistance furnace. Bi 2 V 0.80 Ti 0.20 O 5 . 5−δ (TBV) powders were prepared by solidstate reaction from stoichiometric amounts of the following oxides: Bi 2 O 3 (99%), V 2 O 5 (99%) amd Ti 2 O 3 (99.995%). The starting oxide powders were weighed in defined proportion and ground in acetone medium for 2 h using an agate mortar and pestle to achieve homogenous mixture. • C in alumina crucible for 12 h in air. Calcined powders were again ground and pellets of this mixture were made by compacting these powders in hydraulic press at a load of 12 kN/cm 2 . The pellets of 10 mm × 10 mm × 3 mm so prepared were sintered at 800 • C for 12 h. The electrolyte/glass diffusion couples were prepared by slurry technique. The glass powder was dispersed in ethanol and then deposited between electrolyte plates to make sandwich like structure. The joining thermal treatment of the diffusion couple was given in tubular furnace. YSZ/glass couple was heated from room temperature to 900 • C whereas TBV/glass couple was heated from room temperature to 825 • C. In both the cases, the heating rate was kept at 30 • C /min and dwelling time of 30 min was given. The joined samples were then subjected to heat-treatment at 850 • C in case of high-temperature couples and at 800 • C for low temperature couples for different durations i.e. 5, 100 and 750 h. The prepared diffusion couples were ultrasonically cleaned in acetone. First set of heat-treated samples of diffusion couple were polished mechanically and etched with dilute HF. These polished and etched diffusion couple were analyzed under SEM (EVO-ZEISS). The elemental analysis was done by energy dispersive spectroscopy (EDS) in order to study the interaction and diffusion of ions. From another set of diffusion couple glass seal

Implementation of the X-Means Algorithm on Unemployment Data in West Java

The unemployment rate is a serious issue in many countries, including Indonesia, with impacts that encompass psychological pressure, declining living standards, reduced national income, and diminished workforce quality. High unemployment rates can lead to poverty, resource wastage, and have implications for a country's economic growth. Additionally, inadequate workforce quality is a determining factor in unemployment rates. The objective of this study is to classify unemployment data, specifically in West Java Province, using the X-Means algorithm based on educational levels and districts/cities, in order to examine the fluctuations in unemployment. The research findings indicate fluctuations in the number of unemployed individuals in West Java Province from 2011 to 2022. The low unemployment rate in West Java Province experienced a 66.7% increase in 2014-2015, remained stable in 2017-2019, while the high unemployment rate decreased by 80% in 2014-2015, remained stable in 2017-2019, but increased by 20% in 2020-2022. Furthermore, Bogor Regency consistently recorded the highest unemployment rate in each cluster, with a total of 135,000 unemployed individuals in Bogor Regency in 2017-2019, while the education level with the highest unemployment rate was junior high school graduate