Improving operations efficiency

The research aim was to improve operations efficiency in an organisation by implementing effective time management strategies. The objectives were to identify time management problems, find appropriate solutions relating to issues, design a method to implement the solutions, and recommend the use of more sophisticated machinery and chemicals. Theoretical review used to analyse problems. Qualitative method was used to collect data. Data was gathered through a semi-structured interview with the company’s operations manager. The interview data showed solutions and recommendations for problems facing the organisation, which include scheduled operations time, following the Health and Safety Act 2015, ensuring customer satisfaction, conducting training and inductions programmes for new employees, and recommending sophisticated machinery and chemicals. The recommendations are to follow the operations schedule table and to invest in the latest machinery for heavy-duty operations

Class crystallization: Within reach of industrial workers in India?

Over the years, mostly non-communist union leaders in industries have been encouraging their caste workers to become members of their unions first, and subsequently members of their caste associations. However, their interest in developing workers’ class-consciousness is rather decreasing. In contrast, it is mainly communist union leaders who motivate the workers in their unions to defend their class interests through class action. Besides this, the management’s strategy of recognizing, consulting and seeking the support of the numerical majority of non-communist union leaders and keeping their counterparts in the numerical minority of communist unions away from these activities is nothing but a policy of ‘divide and rule’ that impedes the formation of workers unity and ultimately leads them to confrontation through ‘inter-union conflicts’. Moreover, external forces, especially some caste-based political parties, are keen on organizing their caste workers in unions affiliated to their parties in order to help them become union leaders which eventually results in ‘intra-union conflicts’. Such conflicts thwart the development of workers’ class-consciousness, thereby hindering the process of class crystallization

Alcoholism and the Politics of Total Prohibition in Tamil Nadu State, India: A Historical and Sociological Overview

The consumption of alcohol in one form or other has prevailed throughout the history of the world. The first half of this paper deals with the sociocultural roots of alcoholism and drinking habits in ancient India, besides discussing its socioeconomic implications and impact on several areas of life. In the second half, it presents the history of the implementation and repeal of total prohibition in Tamil Nadu state against the backdrop of incumbent Dravidian political parties and a series of statewide anti-liquor protests during 2016 –2017

Sometimes Sharks Appear in Lakes Too: Tridirectional Insights on Leader Humility and Its Influence on Employee Behavior

Most research on leader humility to date has focused on positive outcomes, portraying humble behaviors as unequivocally beneficial to followers and leaders. However, scant research has examined the detrimental aspects of leader humility. This thesis challenges the consensus that leader humility is largely beneficial for followers and leaders. Using a relationship-cognition research lens, the overall objective of this thesis is to explore the seemingly contradictory (paradoxical), detrimental, and pseudo-beneficial outcomes of leader humility on followers and leaders in an organizational context. The thesis consists of three studies. Adopting a follower-centric approach, Study 1 examines the consequences of follower behavior when followers positively perceive leader humility. This study reveals that leader humility has seemingly contradictory effects on followers' voice behavior. Study 2 examines the consequences of follower behavior when followers positively perceive leader humility but attribute it negatively. The results of this field study reveal that leader humility is ineffective when followers attribute the humility to impression management. Adopting a leader-centric approach, Study 3 examines the consequences of leader behavior when leaders positively perceive their own humility. The results of this study demonstrate that leaders' humble behavior is positively associated with increases in unethical behavior. Thus, this thesis provides novel theoretical contributions and insight into the literature on leader humility. Practically, the thesis offers suggestions to organizations to encourage humility in leaders while simultaneously taking steps to mitigate any negative consequences of humble leader behavior

SQL Injection Prevention Technique Using Cryptography

In our day-to-day life, web applications play an important role such as shopping, making financial transactions, social networking, etc. Most of the business prefer online services instead of in-person services because it is easier for both customers and organizations. Making a web application available to everyone makes it more vulnerable. One of those vulnerabilities is SQL (Structured Query Language) injection. SQL injection is a technique where attackers inject malicious code through user inputs or URLs and gain access to the database. Through this attack, hackers can destroy or change the data present in the database. This paper focuses on how to prevent the SQL injection attacks using five cryptographic algorithms (AES, Triple DES, RSA, Blowfish, and Twofish). Finally, the research evaluates which cryptographic algorithm is most appropriate to prevent SQLIA in web applications

Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures

Efficient parallel implementations of scientific applications on multi-core CPUs with accelerators such as GPUs and Xeon Phis is challenging. This requires - exploiting the data parallel architecture of the accelerator along with the vector pipelines of modern x86 CPU architectures, load balancing, and efficient memory transfer between different devices. It is relatively easy to meet these requirements for highly-structured scientific applications. In contrast, a number of scientific and engineering applications are unstructured. Getting performance on accelerators for these applications is extremely challenging because many of these applications employ irregular algorithms which exhibit data-dependent control-flow and irregular memory accesses. Furthermore, these applications are often iterative with dependency between steps, and thus making it hard to parallelize across steps. As a result, parallelism in these applications is often limited to a single step. Numerical simulation of charged particles beam dynamics is one such application where the distribution of work and memory access pattern at each time step is irregular. Applications with these properties tend to present significant branch and memory divergence, load imbalance between different processor cores, and poor compute and memory utilization. Prior research on parallelizing such irregular applications have been focused around optimizing the irregular, data-dependent memory accesses and control-flow during a single step of the application independent of the other steps, with the assumption that these patterns are completely unpredictable. We observed that the structure of computation leading to control-flow divergence and irregular memory accesses in one step is similar to that in the next step. It is possible to predict this structure in the current step by observing the computation structure of previous steps. In this dissertation, we present novel machine learning based optimization techniques to address the parallel implementation challenges of such irregular applications on different HPC architectures. In particular, we use supervised learning to predict the computation structure and use it to address the control-flow and memory access irregularities in the parallel implementation of such applications on GPUs, Xeon Phis, and heterogeneous architectures composed of multi-core CPUs with GPUs or Xeon Phis. We use numerical simulation of charged particles beam dynamics simulation as a motivating example throughout the dissertation to present our new approach, though they should be equally applicable to a wide range of irregular applications. The machine learning approach presented here use predictive analytics and forecasting techniques to adaptively model and track the irregular memory access pattern at each time step of the simulation to anticipate the future memory access pattern. Access pattern forecasts can then be used to formulate optimization decisions during application execution which improves the performance of the application at a future time step based on the observations from earlier time steps. In heterogeneous architectures, forecasts can also be used to improve the memory performance and resource utilization of all the processing units to deliver a good aggregate performance. We used these optimization techniques and anticipation strategy to design a cache-aware, memory efficient parallel algorithm to address the irregularities in the parallel implementation of charged particles beam dynamics simulation on different HPC architectures. Experimental result using a diverse mix of HPC architectures shows that our approach in using anticipation strategy is effective in maximizing data reuse, ensuring workload balance, minimizing branch and memory divergence, and in improving resource utilization

Gait Velocity Estimation using time interleaved between Consecutive Passive IR Sensor Activations

Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in older adults. It is often assessed clinically, but the assessments occur infrequently and do not allow optimal detection of key health changes when they occur. In this paper, we show that the time gap between activations of a pair of Passive Infrared (PIR) motion sensors installed in the consecutively visited room pair carry rich latent information about a person's gait velocity. We name this time gap transition time and show that despite a six second refractory period of the PIR sensors, transition time can be used to obtain an accurate representation of gait velocity. Using a Support Vector Regression (SVR) approach to model the relationship between transition time and gait velocity, we show that gait velocity can be estimated with an average error less than 2.5 cm/sec. This is demonstrated with data collected over a 5 year period from 74 older adults monitored in their own homes. This method is simple and cost effective and has advantages over competing approaches such as: obtaining 20 to 100x more gait velocity measurements per day and offering the fusion of location-specific information with time stamped gait estimates. These advantages allow stable estimates of gait parameters (maximum or average speed, variability) at shorter time scales than current approaches. This also provides a pervasive in-home method for context-aware gait velocity sensing that allows for monitoring of gait trajectories in space and time