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Industry 4.0 and World Economic Divergence - A novel perspective on the impact of fourth industrial revolution on the world economy
No full textIndustry 4.0 is a current trend of digital transformation and integration of processes into the digital environment using automation, big data, and Internet of Things (IoT). The divergence of the world economy, contrary to the convergence infers the increasing gap between developing and developed countries. Although it is true that there are significant productivity and efficiency gains with the upcoming fourth industrial revolution, it is also essential to examine the differences in the impact of automation on these two economies. The thesis is an attempt to investigate how unanticipatedly Industry 4.0 and the upcoming era of automation supports the divergence of the world economy, contributing to the gap between the developed (Japan, USA, Germany) and developing countries (India, Nigeria, Mexico).
The higher the population, the higher the number of people contributing to economy, has been the centre argument for convergence. But how different is the economic impact, when it is the industrial robots working for the economy and when the country even with low population can achieve equally high output? The paper dives over these topics with 1) comparative analysis where an outlook of Industry 4.0 is observed by examining previous three industrial revolutions 2) macroeconomic analysis, where population demographics, labour redeployment and marginal cost-benefit of Industry 4.0 are inferred to discuss divergence of economies. The transdisciplinary paper uses concepts from economics and business disciplines and concludes with policies for developed and developing countries to prepare for the upcoming transition
Industry 4.0 in Supply Chain: Identifying & Overcoming the Challenges of Industry 4.0 in Supply Chain Management – An Empirical Research Study
No full textThis Research paper focuses on identifying & countering the Challenges of Industry 4.0. Although Industry 4.0 has been a boom of the century, the underlying challenges still need to evaluated and counter measure must be taken in order to have long-term goals satisfied. Therefore, first part of the research focuses on identifying the key challenges and then second part of the research has been dedicated to counter measure. The research contains serious of interview ranging from Industry 4.0 experts to end user of these technologies to validate the identified challenges and counter measure. Thus, the research has a practically backed evidence and real time problem scenarios have been well documented through the course of research. Therefore, this research points out six quantified and qualified Industry 4.0 challenge cluster. They are 1. Economic Challenges, 2. Technical Challenges, 3. Competency Challenges, 4. Cultural Challenges, 5. Managerial Challenges 6. Collaborative challenges. Thus, the identified counter measure falls into four areas of system of Innovation. They are 1. Role of Central Government 2. Co-operation of State Universities and Industries 3. Role of State Government in Promoting Technology Fairs 4. Role of Company Management and Unions. This research also a Supply Chain Limitation which must be considered. Therefore, the research serves as a base guideline for companies in knowing the challenges and provides a direction for the mitigating these challenges through System of Innovation
Learning from (Robot) Failures: Exploring Errors in Child Robot Interaction through a Psychological Lens
No full textAs children develop, they learn to copy from others, interpret their intentions, and evaluate their reliability as sources of information (social learning). Today, these ‘others’ include not only children's peers, parents, or teachers, but also technological devices with which they can interact, including in educational settings. Social robots occupy a particular niche among such technologies, with the ability to embody specific social behaviours such as gaze, gesture, and verbal communication. Nonetheless, the technology underlying the design of social robots remains far from perfect, and opportunities for failures are rife. Understanding how the social capabilities inherent with social robots interact with these inevitable failures is therefore necessary for the design of robust educational interactions with robots. Consequently, the goal of this thesis is to develop an understanding of how errors impact children's social attitudes and behaviour towards social robots. First, a meta-analysis on children's trust in social robots was conducted, creating a theoretical baseline from which to study robot errors. Second, a learning task and measurements for use in child-robot-interaction (cHRI) were developed, through which robot errors could be manipulated and their effect on constructs such as trust, liking, and perceived agency captured. Third, the role of robot errors in a real-world implementation of the learning task was examined. Finally, how current social cognition paradigms can be used to explain children's perceptions of robot errors was explored. Throughout these studies, a theme emerged towards errors not being detrimental to children's perceptions of social robots, at least for more short-term interactions. The significance of robot errors and, more generally, social behaviour when evaluating social robots as tutors is discussed. The thesis concludes with recommendations for how cHRI research can draw from psychology in the design of future cHRI studies
Neural Processing of Emotionally Arousing Stimuli in Older Adults
No full textThe present dissertation aimed to contribute to the attempt of decomposing interindividual aging trajectories and to provide empirical evidence about the potential role of brain aging in emotion processing by using different methodological approaches.
Study 1 revealed attenuated arousal-modulated BOLD signals in older adults with low (vs. high) levels of executive functioning, for both negative and positive emotional stimuli in different brain areas, including bilateral premotor area (BA 6), dorsolateral prefrontal cortex, inferior parietal lobule, and left putamen. Functional connectivity of amygdala and visual cortex with various other brain regions was as well found.
Study 2 revealed that brain functioning related to executive functioning moderates the relation between subjective arousal and level of executive functioning. Moderation effects were found for brain activity in several brain regions including lateral and medial frontal cortex, medial temporal cortex, occipital cortex, insula, and cerebellum. Older adults with brain functioning associated with brain aging and low executive functioning showed high levels of positive as well as negative arousal.
Study 3 investigated changes in subjective negative arousal after a 12-month aerobic intervention training. It revealed that, overall, older adults decreased in negative arousal, most likely due to improvements in emotion regulation. However, one subgroup increased in negative arousal. This subgroup showed high levels of executive functioning and compensatory brain activity at T0.
The preliminary results of study 4 revealed that lower white matter in the frontal cortex went along with higher negative arousal
Heterogeneity in physical activity behavior change – Implications for designing and evaluating digital physical activity interventions targeted at older adults
No full textBackground: Digital physical activity interventions can be effective, but how their components influence complex health behavior change processes has rarely been investigated in older adults.
Objective: Applying principles from social cognitive theory, missing value treatment and person-centered analyses, this thesis aims to tackle three research gaps in the form of barriers to physical activity behavior change in digital interventions targeted at older adults.
Methods: Study 1 covers social-cognitive mechanisms in the effect of tailored, theory-based digital interventions on movement in the physical activity stage of change. In study 2, lifestyle profiles consisting of six self-reported, health-related behaviors were researched using latent profile analysis. Adjusted risk ratios were calculated to identify dropout-vulnerable risk profiles. In Study 3, latent class growth analysis was used to determine trajectory subgroups regarding physical activity and sedentary behavior.
Results: In study 1, the hypothesized positive effects on stage of change were partly mediated by social-cognitive predictor changes. There were heterogenous intervention mechanisms. Four latent health-related lifestyle profiles were identified in study 2. Membership of the “socially inactive lifestyle” profile was associated with an elevated risk of dropping out. Study 3 identified two latent physical activity and sedentary behavior change trajectories, respectively. Significant positive trajectories were only observed in the highly sedentary.
Discussion and Conclusion: This thesis lays out a theoretical and methodological basis of how areas of heterogeneity in the physical activity behavior change process of older adults participating in digital interventions can be analyzed. Characterizing distinct subgroups and their needs can advance tailoring of intervention components and behavior change strategies, and ultimately improve acceptance, retention, and long-term intervention effectiveness
LDPC Codes Incorporating Source, Noise, and Channel Memory
No full textThis thesis discusses how memory of the source, of disturbances, or of the channel can be efficiently dealt with inside the decoding of LDPC codes. Furthermore, how such codes can be optimized for including source memory is also presented.
At the source, the memory is modeled via a Markov chain. The transition probabilities of the model are used at the decoder to estimate the source symbols. Although computed at the decoder, this information is considered to be a-priori information. The a-priori LLR can be directly incorporated into the Tanner graph, a novel simplified computation which provides equal performance to existing methods is shown. A Turbo-like scheme is also proposed where a BCJR and an LDPC decoder decode the source and received sequences iteratively, each utilizing extrinsic information computed by the other. The Turbo-like scheme performs the best at low SNRs. Subsequently, a code design algorithm is provided for obtaining optimized codes for the decoding model with direct additional links in the Tanner graph. For the optimization, density evolution is used. The optimized codes provide steeper performance curves than non-optimized ones.
Thereafter, impulse noise with memory is investigated, which is modeled by the Middleton Class-A model. A Markov model provides the transition probabilities between background and impulsive noise states. A Viterbi decoder estimates the noise sequence and an LDPC decoder estimates the transmitted symbols. Information is iterated between the decoders to improve the overall correction at the receiver. Possibilities for computing the noise states directly at the decoder are also investigated. However, the noise-memory cannot be directly incorporated into the Tanner graph. Lastly, a method is proposed to mitigate channel memory which causes inter-symbol interference using an LDPC decoder. A decision-feedback equalization like structure is used in which the intermediate LDPC decoder results are used for equalization
Quantization and LDPC-based Key Reconciliation for Physical Layer Security
No full textPhysical Layer Security (PHYSEC) uses the inherently random and reciprocal nature of physical (wireless) channels as a source for the generation of symmetric keys at the physical layer. The channel measurements of the two users involved in the key agreement, from which the keys are derived, are corrupted by independent noise components. This leads to key discrepancies which need to be reconciled between the two parties involved in the key agreement before the keys can be used for encryption and authentication.
The focus of this thesis is the key reconciliation step of PHYSEC, with particular emphasis on the two main classes of reconciliation schemes: those based on introducing guard bands during the quantization process and those based on error-correcting codes.
To that end, we first investigate the effect that the choice of the quantization method and associated parameters has on the key agreement rate and on the security of the system.
Our findings show that for medium to high SNRs, good reconciliation performance can be achieved with guard-based methods without compromising security. When the legitimate users experience low SNRs, however, we have found guard-based methods to be unsuitable when used as the sole reconciliation method. This is because, in order to achieve the target reconciliation performance at low SNRs, they would require large guard-band widths, which would have a negative impact not only on the efficiency and key generation rate but also on security by providing an advantage to potential eavesdroppers.
We propose a hybrid reconciliation method that combines guard bands with error-correcting codes which we specifically designed to achieve good performance at low SNRs. As a final result, we provide several Low-Density Parity-Check (LDPC) code ensembles with a Multi-Edge-Type (MET) structure, which we have specifically designed for wireless key reconciliation
Towards Reliable Low-Latency Massive Wireless Communications
No full textOwing to the ever-growing volume of mobile traffic and wireless devices as shown in network traffic reports over the last decades, the wireless mobile technology is required and expected to offer continuous improvements on the major communication performance such as data rate and connection density. Besides expanding such communication performance, various application-centric Quality of Service (QoS) requirements have been raised from a wide range of application fields, imposing on the research community the need of diverse wireless solutions to satisfy such heterogeneous QoS requirements.
In light of the above, this dissertation intends to contribute to the aforementioned trend by addressing three essentials in future wireless systems: massive connectivity, low-latency, and reliability, offering the corresponding algorithm design(s) and quantitative analyses to evaluate the performance. Numerical performance assessments via computer simulations are offered to evaluate the aforementioned proposed methods, illustrating their advantages against existing counterparts
Demand Forecasting in the Fashion Industry: The Shift to AI-Based Methods
No full textThis work aims to analyze and cluster demand forecasting methods and approaches in previous research endeavors which have been recently going through an obvious trend and direction towards a more digital supply chain and automated forecasting methods. For this, developments in the fashion industry are addressed and previous forecasting methods are linked to current market features. Current existing challenges and barriers are identified in order to be able to propose future research areas and framework in demand forecasting in the Fast Fashion Industry
The Molecular Mechanism of Major Histocompatibility Complex Class I Peptide Binding and Exchange
No full textMajor histocompatibility class I (MHC-I) molecules are key to our body's immune defence against pathogens and tumors by presenting the cytosolic peptidome to cytotoxic T lymphocytes (CTLs). MHC-I/peptide complexes are presented on the cell surface, and recognition by CTLs results in a kill signal and the destruction of aberrant cells.
In this thesis, I have described the use of small molecule-assisted refolding of MHC-I proteins for generating their empty, peptide-receptive forms. I have also developed alternative methods for peptide exchange, thermostability and peptide affinity measurements, which can contribute significantly to the understanding of MHC-I selection mechanism and developing reagents for clinical applications.
The classical refolding of MHC-I in vitro has always required full-length peptides, whereas empty forms were nearly impossible to obtain. We demonstrated that both wild type and disulfide bond-stabilized MHC-I molecules can be folded with an excess of an allotype specific dipeptide. To discover specific dipeptides, I have developed a competitive enzyme-linked immunosorbent assay and used it to screen dipeptides and tripeptides that modulate MHC-I refolding. The folded dsMHC-I molecules can later be stripped of dipeptides in a washing process to generate the stable empty forms. Such empty dsMHC-I molecules can be used in rapid multimer generation.
We can also use these empty molecules to fish out peptides from tumor tissues or infected cells before characterizing them using mass spectrometry. We can also compare the peptide affinities from in-vitro assays by monitoring direct binding of the peptide to empty dsMHC-I molecules. We have successfully developed a new assay with potential clinical applications.
Taken together, in the future, we will be able to accelerate the process of neoepitope discovery and provide efficient, and robust solutions to accelerate immunotherapeutic treatments