On the treatment of uncertainty in Innovation Projects

The treatment of uncertainty in innovation projects is a critical aspect that must be addressed to improve project outcomes. This thesis focuses on identifying, measuring, and managing uncertainty in innovation projects, specifically emphasizing perspectives from innovation, risk management, and decision-making. The problematic aspects identified in the literature review include long incubation periods, standardized rules and procedures, non-existent market and market unfamiliarity, fuzziness in the fuzzy front end, team-based dynamic shifting capability, and selecting the right project leader. The research gap identified in the existing literature is the absence of a unified framework or toolbox that comprehensively addresses uncertainty in innovation projects. This thesis aims to fill this gap by proposing a unified toolbox to treat uncertainty effectively. The analytical direction of the research involves identifying the areas of uncertainty, measuring the impact on project outcomes, and developing a toolbox to manage and mitigate those. The research methodology adopted for this study is a qualitative case study approach, utilizing a multiple case study design. Two European Union projects – RESPONDRONE and ASSISTANCE, are selected for conducting a case study analysis. Thematic analysis is employed to derive meaningful insights and patterns from the data gathered during research. From the thematic analysis of the selected cases, five key themes are identified that significantly impact the uncertainty treatment of radical innovation projects. The key themes are- technology and innovation, communication and collaboration, adaptive project management, stakeholder engagement, and risk management. Each theme significantly impacts uncertainty treatment in the four critical areas of uncertainty- market, technological, organizational, and resource. These observations steer the study to see the treatment of uncertainty in innovation projects through the lens of existing literature. An impact assessment flowchart is developed, and a unified toolbox is proposed for better uncertainty treatment by putting things into different perspectives. This thesis concludes that the uncertainty paradigm in radical innovation projects is complex and nuanced. Rather than trying to pinpoint every aspect of it, a better approach for a project team is to understand the common areas of uncertainty generation, measure the impact of an unexpected event as soon as possible and equip themselves with a unified toolbox that can provide them the flexibility to use any tools necessary based on the context of the uncertainty

Long Movie Clip Classification with State-Space Video Models

Most modern video recognition models are designed to operate on short video clips (e.g., 5-10s in length). Because of this, it is challenging to apply such models to long movie understanding tasks, which typically require sophisticated long-range temporal reasoning capabilities. The recently introduced video transformers partially address this issue by using long-range temporal self-attention. However, due to the quadratic cost of self-attention, such models are often costly and impractical to use. Instead, we propose ViS4mer, an efficient long-range video model that combines the strengths of self-attention and the recently introduced structured state-space sequence (S4) layer. Our model uses a standard Transformer encoder for short-range spatiotemporal feature extraction, and a multi-scale temporal S4 decoder for subsequent long-range temporal reasoning. By progressively reducing the spatiotemporal feature resolution and channel dimension at each decoder layer, ViS4mer learns complex long-range spatiotemporal dependencies in a video. Furthermore, ViS4mer is $2.63\times$ faster and requires $8\times$ less GPU memory than the corresponding pure self-attention-based model. Additionally, ViS4mer achieves state-of-the-art results in $7$ out of $9$ long-form movie video classification tasks on the LVU benchmark. Furthermore, we also show that our approach successfully generalizes to other domains, achieving competitive results on the Breakfast and the COIN procedural activity datasets. The code will be made publicly available

Deep Learning Models for Predicting Phenotypic Traits and Diseases from Omics Data

Computational analysis of high-throughput omics data, such as gene expressions, copy number alterations and DNA methylation (DNAm), has become popular in disease studies in recent decades because such analyses can be very helpful to predict whether a patient has certain disease or its subtypes. However, due to the high-dimensional nature of the data sets with hundreds of thousands of variables and very small number of samples, traditional machine learning approaches, such as support vector machines (SVMs) and random forests, have limitations to analyze these data efficiently. In this chapter, we reviewed the progress in applying deep learning algorithms to solve some biological questions. The focus is on potential software tools and public data sources for the tasks. Particularly, we show some case studies using deep neural network (DNN) models for classifying molecular subtypes of breast cancer and DNN-based regression models to account for interindividual variation in triglyceride concentrations measured at different visits of peripheral blood samples using DNAm profiles. We show that integration of multi-omics profiles into DNN-based learning methods could improve the prediction of the molecular subtypes of breast cancer. We also demonstrate the superiority of our proposed DNN models over the SVM model for predicting triglyceride concentrations

FDTD Analysis Fiber Optic SPR Biosensor for DNA Hybridization: A Numerical Demonstration with Graphene

This article illustrates a design and finite difference time domain (FDTD) method based on analysis of fiber optic surface plasmon resonance (SPR) biosensor for biomedical application especially for DNA-DNA hybridization. The fiber cladding at the middle portion is constructed with the proposed hybrid of gold (Au), graphene, and a sensing medium. This sensor can be recognized adsorption of DNA biomolecules onto sensing medium of PBS saline using attenuated total reflection (ATR) technique. The refractive index (RI) is varied owing to the adsorption of different concentration of biomolecules.  Result states that the sensitivity with a monolayer of graphene will be improved up to 40% than bare graphene layer. Owing to increased adsorption capability of DNA molecules on graphene, sensitivity increases compared to the conventional gold thin film SPR biosensor. Numerical analysis shows that the variation of the SPR angle for mismatched DNA strands is quite negligible, whereas that for complementary DNA strands is considerable, which is essential for proper detection of DNA hybridization.  Finally, the effect of Electric field distribution on inserting graphene layer is analyzed incorporating the FDTD technique by using Lumerical FDTD solution software

Roles of Stakeholders Towards Project Success: A Conceptual Study

Stakeholder plays significant roles in project success. They ensure clear communication of project goals, contribute to decision-making, and demonstrate commitment, increasing the likelihood of successful outcomes. They also act as advocates within their organizations, generating buy-in and support. The main purpose of this paper is to identify and discuss the roles of shareholders in a project success. The paper is conceptual in nature and uses a number literatures ranging from 2007 to 2023 from a good number of journals. After scrutinized the literature review, the paper concludes a number of findings. The findings implies that stakeholders in a project is crucial for its success and sustainability. They play a significant role in ensuring the performance of the project. Project managers need to acquire stakeholder management skills to address the communication requirements of stakeholders. This is important for the success of the project. The paper recommend that policymakers, practitioners and academia have to ensure the expectations and make a balance among the stakeholders

A Maximum Flow-Based Approach to Prioritize Drugs for Drug Repurposing of Chronic Diseases

The discovery of new drugs is required in the time of global aging and increasing populations. Traditional drug development strategies are expensive, time-consuming, and have high risks. Thus, drug repurposing, which treats new/other diseases using existing drugs, has become a very admired tactic. It can also be referred to as the re-investigation of the existing drugs that failed to indicate the usefulness for the new diseases. Previously published literature used maximum flow approaches to identify new drug targets for drug-resistant infectious diseases but not for drug repurposing. Therefore, we are proposing a maximum flow-based protein–protein interactions (PPIs) network analysis approach to identify new drug targets (proteins) from the targets of the FDA (Food and Drug Administration) drugs and their associated drugs for chronic diseases (such as breast cancer, inflammatory bowel disease (IBD), and chronic obstructive pulmonary disease (COPD)) treatment. Experimental results showed that we have successfully turned the drug repurposing into a maximum flow problem. Our top candidates of drug repurposing, Guanidine, Dasatinib, and Phenethyl Isothiocyanate for breast cancer, IBD, and COPD were experimentally validated by other independent research as the potential candidate drugs for these diseases, respectively. This shows the usefulness of the proposed maximum flow approach for drug repurposing

A Maximum Flow-Based Approach to Prioritize Drugs for Drug Repurposing of Chronic Diseases

The discovery of new drugs is required in the time of global aging and increasing populations. Traditional drug development strategies are expensive, time-consuming, and have high risks. Thus, drug repurposing, which treats new/other diseases using existing drugs, has become a very admired tactic. It can also be referred to as the re-investigation of the existing drugs that failed to indicate the usefulness for the new diseases. Previously published literature used maximum flow approaches to identify new drug targets for drug-resistant infectious diseases but not for drug repurposing. Therefore, we are proposing a maximum flow-based protein–protein interactions (PPIs) network analysis approach to identify new drug targets (proteins) from the targets of the FDA (Food and Drug Administration) drugs and their associated drugs for chronic diseases (such as breast cancer, inflammatory bowel disease (IBD), and chronic obstructive pulmonary disease (COPD)) treatment. Experimental results showed that we have successfully turned the drug repurposing into a maximum flow problem. Our top candidates of drug repurposing, Guanidine, Dasatinib, and Phenethyl Isothiocyanate for breast cancer, IBD, and COPD were experimentally validated by other independent research as the potential candidate drugs for these diseases, respectively. This shows the usefulness of the proposed maximum flow approach for drug repurposing

Human identification based on color stimuli

Human identification by using Electroencephalogram is becoming promising field and reliable to improve security systems. It is difficult to acquire EEG at a certain mental condition always such as concentration or relaxation. This paper represents a simple model to identify individuals and finding most effective primary color by using features of EEG by means of color stimuli. A comparison between primary and secondary colors for identification has also been made. Standard additive primary colors blue, green, red and one secondary color yellow were selected for experiment. Four neural networks were built by extracting various features of EEG in the domain of time and frequency. All artificial neural networks showed satisfactory performance with minimum mean square error for identification. Among the four selected colors blue color based ANN showed minimum mean square error of 6.238×10-08.</p

An Automated Music Selector Derived from Weather Condition and its Impact on Human Psychology

Sometimes it is disquieting to generate a playlist to listen music for a specific moment. Though listening of music basically depends on our mood and it’s also been said that there exists a relation between our mood and weather, so our approach is to build an automated system to create a music playlist based on users mood and defined weather. Method is to measure the weight of each music files respect to defined mood and weather by using data mining algorithms

A deep neural network based regression model for triglyceride concentrations prediction using epigenome-wide DNA methylation profiles

Abstract Background Epigenetic modification has an effect on gene expression under the environmental alteration, but it does not change corresponding genome sequence. DNA methylation (DNAm) is one of the important epigenetic mechanisms. DNAm variations could be used as epigenetic markers to predict and account for the change of many human phenotypic traits, such as cancer, diabetes, and high blood pressure. In this study, we built deep neural network (DNN) regression models to account for interindividual variation in triglyceride concentrations measured at different visits of peripheral blood samples using epigenome-wide DNAm profiles. Results We used epigenome-wide DNAm profiles of before and after medication interventions (called pretreatment and posttreatment, respectively) to predict triglyceride concentrations for peripheral blood draws at visit 2 (using pretreatment data) and at visit 4 (using both pretreatment and posttreatment data). Our experimental results showed that DNN models can predict triglyceride concentrations for blood draws at visit 4 using pretreatment and posttreatment DNAm data more accurately than for blood draws at visit 2 using pretreatment DNAm data. Furthermore, we got the best prediction results when we used pretreatment DNAm data to predict triglyceride concentrations for blood draws at visit 4, which suggests a long-term epigenetic effect on phenotypic traits. We compared the prediction performances of our proposed DNN models with that of support vector machine (SVM). This comparison showed that our DNN models achieved better prediction performance than did SVM. Conclusions We demonstrated the superiority of our proposed DNN models over the SVM model for predicting triglyceride concentrations. This study also suggests that the DNN approach has advantages over other traditional machine-learning methods to model high-dimensional epigenome-wide DNAm data and other genomic data