A Systemic Model To Augment Consulting Competencies For Success In a Remote And Multicultural Work Environment

This dissertation aimed to identify gaps in the consulting core competencies and challenges faced in remote and multicultural work environments. The survey research method was utilized to gather consultants\u27 input on core competencies and challenges based on their experiences. This research was only applicable to those consultants who could perform their roles and responsibilities in a remote and multicultural work environment. Based on the research analysis, a core competency framework is shared to enhance consultant’s core competencies to make them successful. The shared framework can help understand some of the industry’s critical problems and provide valuable knowledge that will help look at them from a different angle. As a result, consulting organizations can maximize their consultants’ competencies to sustain their consulting brand and provide remote business continuity to their valuable clients. This research will benefit the consulting organizations, consultants, and clients operating in a remote and multicultural work environment. Additionally, future consultants can capitalize on this research by entering the consulting industry in the forthcoming years

THE EMISSION AND PARTICULATE MATTER OXIDATION PERFORMANCE OF A SCR CATALYST ON A DIESEL PARTICULATE FILTER WITH A DOWNSTREAM SCR

Selective catalytic reduction (SCR) systems along with a NH₃ slip control catalyst (ASC) offers NOₓ conversion efficiency \u3e90 % with NH₃ slip \u3c 20 ppm. However, future heavy duty diesel (HDD) engines are designed for higher engine-out NOₓ to improve fuel consumption. Consequently, there is a strong desire to further improve the NOₓ reduction performance of SCR systems, to meet the 2015 California Optional Low NOₓ Standard. SCR catalysts on a diesel particulate filter provide an effective solution to reduce NOₓ and PM using a single aftertreatment device. It also provides an opportunity to increase the SCR volume to achieve NOₓ conversion efficiency \u3e95 %. A downstream SCR catalyst substrate can be used to get additional NOx conversion by using the SCRF® outlet NH₃ to increase the cumulative NOₓ conversion of the system. In this study, NOₓ reduction, NH₃ slip and PM oxidation performance of a Cu-zeolite SCRF® with a downstream Cu-zeolite SCR were investigated based on engine experimental data at steady state conditions. The experimental data were collected at varying SCRF® inlet temperatures, space velocities, inlet NOₓ concentrations, NO₂/ NOₓ ratios at ammonia to NOx ratios (ANR) between 1.02 to 1.10. The results demonstrated that the SCRF® with downstream SCR together can achieve NOₓ conversion efficiency \u3e 98% at ANRs between 1.02 – 1.10 (which may have been due to measurement inaccuracies in downstream SCRF®/SCRdata), for the inlet temperature range of 200 – 370°C, space velocity in the range of 10 to 34 k/hr and inlet NO₂/ NOₓ in the range of 0.3 – 0.5. However, NH₃ slip from the SCRF® decreases and NOₓ concentration downstream of the SCRF® increases with the oxidation of PM in the SCRF®. The PM oxidation kinetics are affected by the deNOₓ reactions, hence, the SCRF® with urea dosing showed ~80 % lower reaction rates during passive oxidation when compared to the production CPF. The effect of varying fuel rail injection pressure on the primary particle diameter and on the Elemental Carbon (EC) and Organic Carbon (OC) fraction of the total carbon was also studied. The primary particle diameter was found to be in the range of 28-30 nm with no effect of the variation in fuel rail injection pressure on it. The OC part of the Total Carbon (TC) did not vary significantly with fuel rail injection pressure. The EC content increased with decrease in fuel rail injection pressure

A Fixed-Parameter Tractable Algorithm for Counting Markov Equivalence Classes with the same Skeleton

Causal DAGs (also known as Bayesian networks) are a popular tool for encoding conditional dependencies between random variables. In a causal DAG, the random variables are modeled as vertices in the DAG, and it is stipulated that every random variable is independent of its ancestors conditioned on its parents. It is possible, however, for two different causal DAGs on the same set of random variables to encode exactly the same set of conditional dependencies. Such causal DAGs are said to be Markov equivalent, and equivalence classes of Markov equivalent DAGs are known as Markov Equivalent Classes (MECs). Beautiful combinatorial characterizations of MECs have been developed in the past few decades, and it is known, in particular that all DAGs in the same MEC must have the same ''skeleton'' (underlying undirected graph) and v-structures (induced subgraph of the form $a\rightarrow b \leftarrow c$). These combinatorial characterizations also suggest several natural algorithmic questions. One of these is: given an undirected graph $G$ as input, how many distinct Markov equivalence classes have the skeleton $G$? Much work has been devoted in the last few years to this and other closely related problems. However, to the best of our knowledge, a polynomial time algorithm for the problem remains unknown. In this paper, we make progress towards this goal by giving a fixed parameter tractable algorithm for the above problem, with the parameters being the treewidth and the maximum degree of the input graph $G$. The main technical ingredient in our work is a construction we refer to as shadow, which lets us create a "local description'' of long-range constraints imposed by the combinatorial characterizations of MECs.Comment: 75 pages, 2 Figure

Confidential Machine Learning on Untrusted Platforms: a Survey

With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and models are in danger of unauthorized access, misuse, and privacy compromises. A relatively new body of research confidentially trains machine learning models on protected data to address these concerns. In this survey, we summarize notable studies in this emerging area of research. With a unified framework, we highlight the critical challenges and innovations in outsourcing machine learning confidentially. We focus on the cryptographic approaches for confidential machine learning (CML), primarily on model training, while also covering other directions such as perturbation-based approaches and CML in the hardware-assisted computing environment. The discussion will take a holistic way to consider a rich context of the related threat models, security assumptions, design principles, and associated trade-offs amongst data utility, cost, and confidentiality

Neural network based novel controller for hybrid energy storage system for electric vehicles

This manuscript deals with the various control strategies of storage system for an electrical vehicle. High demands in the electrical systems in the field of transportations leads to various challenges and more precise control and regulations techniques. Apart from the conventional grid system now a days the integration of renewable energy systems like solar, wind and fuel cell system leads to more complex system but these system shares the load from conventional generating system. This paper deals with the study and control aspects of the electrical vehicles associated with hybrid energy storage (HES) systems. In general, when systems are integrated with the main grid there are more distortions and ripples in the system. To reduce these distortions various control techniques are used. This paper proposes a neural network-based PI (NNPI) controller for HES system for electric vehicles for better distortion less outputs