Knowledge management framework based on brain models and human physiology

The life of humans and most living beings depend on sensation and perception for the best assessment of the surrounding world. Sensorial organs acquire a variety of stimuli that are interpreted and integrated in our brain for immediate use or stored in memory for later recall. Among the reasoning aspects, a person has to decide what to do with available information. Emotions are classifiers of collected information, assigning a personal meaning to objects, events and individuals, making part of our own identity. Emotions play a decisive role in cognitive processes as reasoning, decision and memory by assigning relevance to collected information. The access to pervasive computing devices, empowered by the ability to sense and perceive the world, provides new forms of acquiring and integrating information. But prior to data assessment on its usefulness, systems must capture and ensure that data is properly managed for diverse possible goals. Portable and wearable devices are now able to gather and store information, from the environment and from our body, using cloud based services and Internet connections. Systems limitations in handling sensorial data, compared with our sensorial capabilities constitute an identified problem. Another problem is the lack of interoperability between humans and devices, as they do not properly understand human’s emotional states and human needs. Addressing those problems is a motivation for the present research work. The mission hereby assumed is to include sensorial and physiological data into a Framework that will be able to manage collected data towards human cognitive functions, supported by a new data model. By learning from selected human functional and behavioural models and reasoning over collected data, the Framework aims at providing evaluation on a person’s emotional state, for empowering human centric applications, along with the capability of storing episodic information on a person’s life with physiologic indicators on emotional states to be used by new generation applications

Accountability Infrastructure: How to implement limits on platform optimization to protect population health

Attention capitalism has generated design processes and product development decisions that prioritize platform growth over all other considerations. To the extent limits have been placed on these incentives, interventions have primarily taken the form of content moderation. While moderation is important for what we call "acute harms," societal-scale harms -- such as negative effects on mental health and social trust -- require new forms of institutional transparency and scientific investigation, which we group under the term accountability infrastructure. This is not a new problem. In fact, there are many conceptual lessons and implementation approaches for accountability infrastructure within the history of public health. After reviewing these insights, we reinterpret the societal harms generated by technology platforms through reference to public health. To that end, we present a novel mechanism design framework and practical measurement methods for that framework. The proposed approach is iterative and built into the product design process, and is applicable for both internally-motivated (i.e. self regulation by companies) and externally-motivated (i.e. government regulation) interventions for a range of societal problems, including mental health. We aim to help shape a research agenda of principles for the design of mechanisms around problem areas on which there is broad consensus and a firm base of support. We offer constructive examples and discussion of potential implementation methods related to these topics, as well as several new data illustrations for potential effects of exposure to online content.Comment: 63 pages, 5 tables and 6 figure

Computational and Causal Approaches on Social Media and Multimodal Sensing Data: Examining Wellbeing in Situated Contexts

A core aspect of our lives is often embedded in the communities we are situated in. The interconnectedness of our interactions and experiences intertwines our situated context with our wellbeing. A better understanding of wellbeing will help us devise proactive and tailored support strategies. However, existing methodologies to assess wellbeing suffer from limitations of scale and timeliness. These limitations are surmountable by social and ubiquitous technologies. Given its ubiquity and wide use, social media can be considered a “passive sensor” that can act as a complementary source of unobtrusive, real-time, and naturalistic data to infer wellbeing. This dissertation leverages social media in concert with multimodal sensing data, which facilitate analyzing dense and longitudinal behavior at scale. This work adopts machine learning, natural language, and causal inference analysis to infer wellbeing of individuals and collectives, particularly in situated communities, such as college campuses and workplaces. Before incorporating sensing modalities in practice, we need to account for confounds. One such confound that might impact behavior change is the phenomenon of “observer effect” --- that individuals may deviate from their typical or otherwise normal behavior because of the awareness of being “monitored”. I study this problem by leveraging the potential of longitudinal and historical behavioral data through social media. Focused on a multimodal sensing study, I conduct a causal study to measure observer effect in social media behavior, and explain the observations through existing theory in psychology and social science. The findings provide recommendations to correcting biases due to observer effect in social media sensing for human behavior and wellbeing. The novelties and contributions of this dissertation are four-fold. First, I use social media data that uniquely captures the behavior of situated communities. Second, I adopt theory-driven computational and causal methods to make conclusive research claims on wellbeing dynamics. Third, I address major challenges with methods to combine social media with multimodal sensing data for a comprehensive understanding of human behavior. Fourth, I draw interpretations and explanations of online-data-driven offline inferences. This dissertation situates the findings in an interdisciplinary context, including psychology and social science, and bears implications from theoretical, practical, design, methodological, and ethical perspectives catering to various stakeholders, including researchers, practitioners, and policymakers.Ph.D

Experimental and Numerical Analysis of Ethanol Fueled HCCI Engine

Presently, the research on the homogeneous charge compression ignition (HCCI) engines has gained importance in the field of automotive power applications due to its superior efficiency and low emissions compared to the conventional internal combustion (IC) engines. In principle, the HCCI uses premixed lean homogeneous charge that auto-ignites volumetrically throughout the cylinder. The homogeneous mixture preparation is the main key to achieve high fuel economy and low exhaust emissions from the HCCI engines. In the recent past, different techniques to prepare homogeneous mixture have been explored. The major problem associated with the HCCI is to control the auto-ignition over wide range of engine operating conditions. The control strategies for the HCCI engines were also explored. This dissertation investigates the utilization of ethanol, a potential major contributor to the fuel economy of the future. Port fuel injection (PFI) strategy was used to prepare the homogeneous mixture external to the engine cylinder in a constant speed, single cylinder, four stroke air cooled engine which was operated on HCCI mode. Seven modules of work have been proposed and carried out in this research work to establish the results of using ethanol as a potential fuel in the HCCI engine. Ethanol has a low Cetane number and thus it cannot be auto-ignited easily. Therefore, intake air preheating was used to achieve auto-ignition temperatures. In the first module of work, the ethanol fueled HCCI engine was thermodynamically analysed to determine the operating domain. The minimum intake air temperature requirement to achieve auto-ignition and stable HCCI combustion was found to be 130 °C. Whereas, the knock limit of the engine limited the maximum intake air temperature of 170 °C. Therefore, the intake air temperature range was fixed between 130-170 °C for the ethanol fueled HCCI operation. In the second module of work, experiments were conducted with the variation of intake air temperature from 130-170 °C at a regular interval of 10 °C. It was found that, the increase in the intake air temperature advanced the combustion phase and decreased the exhaust gas temperature. At 170 °C, the maximum combustion efficiency and thermal efficiency were found to be 98.2% and 43% respectively. The NO emission and smoke emissionswere found to be below 11 ppm and 0.1% respectively throughout this study. From these results of high efficiency and low emissions from the HCCI engine, the following were determined using TOPSIS method. They are (i) choosing the best operating condition, and (ii) which input parameter has the greater influence on the HCCI output. In the third module of work, TOPSIS - a multi-criteria decision making technique was used to evaluate the optimum operating conditions. The optimal HCCI operating condition was found at 70% load and 170 °C charge temperature. The analysis of variance (ANOVA) test results revealed that, the charge temperature would be the most significant parameter followed by the engine load. The percentage contribution of charge temperature and load were63.04% and 27.89% respectively. In the fourth module of work, the GRNN algorithm was used to predict the output parameters of the HCCI engine. The network was trained, validated, and tested with the experimental data sets. Initially, the network was trained with the 60% of the experimental data sets. Further, the validation and testing of the network was done with each 20% data sets. The validation results predicted that, the output parameters those lie within 2% error. The results also showed that, the GRNN models would be advantageous for network simplicity and require less sparse data. The developed new tool efficiently predicted the relation between the input and output parameters. In the fifth module of work, the EGR was used to control the HCCI combustion. An optimum of 5% EGR was found to be optimum, further increase in the EGR caused increase in the hydrocarbon (HC) emissions. The maximum brake thermal efficiency of 45% was found for 170 °C charge temperature at 80% engine load. The NO emission and smoke emission were found to be below 10 ppm and 0.61% respectively. In the sixth module of work, a hybrid GRNN-PSO model was developed to optimize the ethanol-fueled HCCI engine based on the output performance and emission parameters. The GRNN network interpretive of the probability estimate such that it can predict the performance and emission parameters of HCCI engine within the range of input parameters. Since GRNN cannot optimize the solution, and hence swarm based adaptive mechanism was hybridized. A new fitness function was developed by considering the six engine output parameters. For the developed fitness function, constrained optimization criteria were implemented in four cases. The optimum HCCI engine operating conditions for the general criteria were found to be 170 °C charge temperature, 72% engine load, and 4% EGR. This model consumed about 60-75 ms for the HCCI engine optimization. In the last module of work, an external fuel vaporizer was used to prepare the ethanol fuel vapour and admitted into the HCCI engine. The maximum brake thermal efficiency of 46% was found for 170 °C charge temperature at 80% engine load. The NO emission and smoke emission were found to be below 5 ppm and 0.45% respectively. Overall, it is concluded that, the HCCI combustion of sole ethanol fuel is possible with the charge heating only. The high load limit of HCCI can be extended with ethanol fuel. High thermal efficiency and low emissions were possible with ethanol fueled HCCI to meet the current demand

VRSC 2021 Conference Proceedings

The biennial conference aims to catalyze ideas and innovation between academia, practice, NGOs and government agencies who work to address analysis, planning, valuation, design and management of visual resources. The aim of the 2021 Virtual Conference is to share ideas and discuss the issues associated with the assessment and protection of visual resources in an era of major landscape change - regionally, national and globally

Aeronautical engineering: A continuing bibliography with indexes (supplement 202)

This bibliography lists 447 reports, articles and other documents introduced into the NASA scientific and technical information system in June 1986

2017 Annual Research Symposium Abstract Book

2017 annual volume of abstracts for science research projects conducted by students at Trinity College