Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

Discipline
Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 19801 - 19830 of 63093

Full-Text Articles in Computer Sciences

Characteristics Of Authentic Entrepreneurial Leadership Within Information Technology Startups In Kenya, Christine Mukhwana Jan 2021

Characteristics Of Authentic Entrepreneurial Leadership Within Information Technology Startups In Kenya, Christine Mukhwana

Walden Dissertations and Doctoral Studies

In Kenya, over 60% of nascent entrepreneurs start businesses with minimal leadership experience, engendering founder-CEOs who cannot sustain a business beyond its formative years. The problem is the nascent entrepreneurs’ lack of understanding of effective leadership. The purpose of this qualitative, phenomenological study was to develop a more nuanced understanding of authentic leadership behavior from the perspective of nascent entrepreneurs. Authentic leadership theory and the three-factor model were used to conceptualize the study. The research question focused on nascent entrepreneurs’ lived experiences and understanding of authentic leadership. The participants in the study were entrepreneurs from the information technology industry in …

Go to article

Meaningful Use Criteria And Staff Accountability In An Office Setting, Marcia Ionie Pender Jan 2021

Meaningful Use Criteria And Staff Accountability In An Office Setting, Marcia Ionie Pender

Walden Dissertations and Doctoral Studies

Proper documentation for meaningful use (MU) criteria within electronic health records (EHRs) was identified as an issue for office staff at a local primary care office in a metropolitan area of Central Florida. The project question addressed the local gap in knowledge about MU standards necessary to ensure correct documentation of EHRs. The purpose of this doctoral project was to provide an educational program for staff to ensure compliance with the HITECH Act of 2009. Lewin’s Change Theory and Knowles Theory of Adult learning were the conceptual foundations for the educational program. The project question was to determine whether a …

Go to article

The Effect Of Social Media Use On Physical Isolation In Individuals With Borderline Personality Disorder, Davena Limitless Longshore Jan 2021

The Effect Of Social Media Use On Physical Isolation In Individuals With Borderline Personality Disorder, Davena Limitless Longshore

Walden Dissertations and Doctoral Studies

Individuals with borderline personality disorder (BPD) experience extreme interpersonal conflict, crippling their ability to sustain successful relationships. Consequently, clinicians within the psychological field face difficulty in devising treatments plans which can assist these individuals with suffering minimal relationship loss. The purpose of this study was to understand how current technological methods of social interaction affect individuals who suffer from BPD to improve treatment outcomes. This research was guided by the principles of attachment theory and social baseline theory. A quantitative correlational design using social network analysis and multiple regression analysis was used to examine data from surveys. Participants were solicited …

Go to article

Strategies For Applying Electronic Health Records To Improve Patient Care And Increase Profitability, Fritzgerald Paul Jan 2021

Strategies For Applying Electronic Health Records To Improve Patient Care And Increase Profitability, Fritzgerald Paul

Walden Dissertations and Doctoral Studies

AbstractIneffective strategies to implement electronic health record keeping systems can negatively impact patient care and increase expenses. Hospital administrators and primary care physicians care about this problem because they would be penalized for not meeting meaningful use guidelines. Grounded in the information systems success model, the purpose of this qualitative multiple case study was to explore electronic health record (EHR) implementation strategies primary care physicians use to improve patient care and increase profitability. The participants comprised five primary care physicians involved in the effective implementation of an EHR application in the central coast region of California. Data were collected from …

Go to article

Strategies To Monitor And Deter Cyberloafing In Small Businesses: A Case Study, Veronica Pugh Dooly Jan 2021

Strategies To Monitor And Deter Cyberloafing In Small Businesses: A Case Study, Veronica Pugh Dooly

Walden Dissertations and Doctoral Studies

Some information technology (IT) managers working for small businesses are struggling to monitor and deter cyberloafing. Strategies are needed to help IT practitioners to discourage cyberloafing and improve productivity while maintaining employee satisfaction. Grounded in adaptive structuration theory, the purpose of this qualitative multiple-case study was to explore strategies some small business IT managers use to monitor and deter cyberloafing. The participants were nine IT managers who successfully implemented cyberloafing monitoring and deterrence strategies in the United States. Data were collected via semistructured interviews and organization employee policy handbooks (n = 4) provided by the participants. Data were analyzed using …

Go to article

Strategies For Integrating The Internet Of Things In Educational Institutions, Anthony Kofi Harvey Jan 2021

Strategies For Integrating The Internet Of Things In Educational Institutions, Anthony Kofi Harvey

Walden Dissertations and Doctoral Studies

The introduction of the Internet of Things (IoT) into educational institutions has necessitated the integration of IoT devices in the information technology (IT) infrastructural environment of educational institutions. Many IT leaders at educational institutions, however, lack strategies for integrating and deploying IoT devices in their institutions, which has resulted in numerous security breaches. The purpose of this study was to explore security strategies adopted by IT administrators to prevent data breaches resulting from the integration of IoT devices in their educational institutions. The diffusion of innovations theory served as the conceptual framework for this qualitative multiple case study. Eleven IT …

Go to article

Consumers Perspectives On Using Biometric Technology With Mobile Banking, Rodney Alston Clark Jan 2021

Consumers Perspectives On Using Biometric Technology With Mobile Banking, Rodney Alston Clark

Walden Dissertations and Doctoral Studies

The need for applying biometric technology in mobile banking is increasing due to emerging security issues, and many banks’ chief executive officers have integrated biometric solutions into their mobile application protocols to address these evolving security risks. This quantitative study was performed to evaluate how the opinions and beliefs of banking customers in the Mid-Atlantic region of the United States might influence their adoption of mobile banking applications that included biometric technology. The research question was designed to explore how performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), perceived credibility (PC), and task-technology fit (TTF) affected …

Go to article

Cyberattacks Strategy For Nonprofit Organizations, Yawo Obimpe Kondo Jan 2021

Cyberattacks Strategy For Nonprofit Organizations, Yawo Obimpe Kondo

Walden Dissertations and Doctoral Studies

Information system security managers (ISSM) in nonprofits face increased cyberattack cases because nonprofits often use basic technology to save on costs. Nonprofit owners and managers need solutions to secure their data from cyberattacks. Grounded in the general systems theory, the purpose of this qualitative multiple case study was to explore strategies ISSMs at nonprofit organizations employ to protect against cyberattacks. Participants included five IT managers and directors of information technology in charge of security management in nonprofit organizations in Maryland, the District of Columbia, and Virginia. Data was generated through interviews and reviews of archival documents. The data analysis technique …

Go to article

Successful Strategies For Adopting Open-Source Software, Balla Barro Jan 2021

Successful Strategies For Adopting Open-Source Software, Balla Barro

Walden Dissertations and Doctoral Studies

Middle-level information systems managers (ISMs) in small-scale organizations often have challenges in determining the most appropriate strategy for adopting open-source software (OSS) in their organizations. The ISMs need to determine the right strategy for adopting OSS to protect the organizations’ information technology infrastructure. Grounded in the diffusion of innovation theory, the objective of this multiple case study was to explore strategies middle-level ISMs used to adopt OSS in their small-scale organizations. Six middle-level ISMs from three small-scale organizations with experience of not less than six years in OSS adoption strategy in the city of Frederick, Maryland, shared their strategies during …

Go to article

Factors Affecting The Productivity And Satisfaction Of Virtual Workers, Teresa Peoples Jan 2021

Factors Affecting The Productivity And Satisfaction Of Virtual Workers, Teresa Peoples

Walden Dissertations and Doctoral Studies

Advancements in technology have ushered in new digital enterprises, shifting the trend from a conventional workforce to a virtual workforce and giving rise to new challenges for managers. However, the factors affecting virtual worker productivity and job satisfaction are not well known. The purpose of this study was to identify these factors and determine what managers can do to enhance them. The theoretical foundation for this study included sociotechnical theory and content theory as they relate to the motivation and communication needs of the virtual workforce. This qualitative phenomenological study was an exploration of factors that affect virtual workers' productivity …

Go to article

Maritime Cybersecurity Strategies For Information Technology Specialists, Angela Mizelle Griffin Jan 2021

Maritime Cybersecurity Strategies For Information Technology Specialists, Angela Mizelle Griffin

Walden Dissertations and Doctoral Studies

Dependence on digital technology increases cyber-related risks for maritime industries. As a result, the U.S. Coast Guard network is challenged with maritime cybersecurity, both economically and for national security. Grounded in the general systems theory, the purpose of this multiple case study was to explore strategies information technology (IT) specialists use to implement standard practices for ensuring cyber security. The participants included six IT specialists who have successfully implemented standard practices for maritime organizations in Virginia and West Virginia. Data were collected from individual semistructured interviews and a review of 12 external organizational documents available to the public from IT …

Go to article

Ssentiaa: A Self-Supervised Sentiment Analyzer For Classification From Unlabeled Data, Salim Sazzed, Sampath Jayarathna Jan 2021

Ssentiaa: A Self-Supervised Sentiment Analyzer For Classification From Unlabeled Data, Salim Sazzed, Sampath Jayarathna

Computer Science Faculty Publications

In recent years, supervised machine learning (ML) methods have realized remarkable performance gains for sentiment classification utilizing labeled data. However, labeled data are usually expensive to obtain, thus, not always achievable. When annotated data are unavailable, the unsupervised tools are exercised, which still lag behind the performance of supervised ML methods by a large margin. Therefore, in this work, we focus on improving the performance of sentiment classification from unlabeled data. We present a self-supervised hybrid methodology SSentiA (Self-supervised Sentiment Analyzer) that couples an ML classifier with a lexicon-based method for sentiment classification from unlabeled data. We first introduce LRSentiA …

Go to article

Understanding And Predicting Retractions Of Published Work, Sai Ajay Modukuri, Sarah Rajtmajer, Anna Cinzia Squicciarini, Jian Wu, C. Lee Giles Jan 2021

Understanding And Predicting Retractions Of Published Work, Sai Ajay Modukuri, Sarah Rajtmajer, Anna Cinzia Squicciarini, Jian Wu, C. Lee Giles

Computer Science Faculty Publications

Recent increases in the number of retractions of published papers reflect heightened attention and increased scrutiny in the scientific process motivated, in part, by the replication crisis. These trends motivate computational tools for understanding and assessment of the scholarly record. Here, we sketch the landscape of retracted papers in the Retraction Watch database, a collection of 19k records of published scholarly articles that have been retracted for various reasons (e.g., plagiarism, data error). Using metadata as well as features derived from full-text for a subset of retracted papers in the social and behavioral sciences, we develop a random forest classifier …

Go to article

Recognizing Figure Labels In Patents, Ming Gong, Xin Wei, Diane Oyen, Jian Wu, Martin Gryder Jan 2021

Recognizing Figure Labels In Patents, Ming Gong, Xin Wei, Diane Oyen, Jian Wu, Martin Gryder

Computer Science Faculty Publications

Scientific documents often contain significant information in figures. The United States Patent and Trademark Office (USPTO) awards thousands of patents each week, with each patent containing on the order of a dozen figures. The information conveyed by these figures typically include a drawing or diagram, a label, caption and reference text within the document. Yet associating the short bits of text to the figure is challenging when labels are embedded within the figure, as they typically are in patents. Using patents as a testbench, this paper highlights an open challenge in analyzing all of the information presented in scientific/technical documents …

Go to article

Extractive Research Slide Generation Using Windowed Labeling Ranking, Athar Sefid, Prasenjit Mitra, Jian Wu, C. Lee Giles Jan 2021

Extractive Research Slide Generation Using Windowed Labeling Ranking, Athar Sefid, Prasenjit Mitra, Jian Wu, C. Lee Giles

Computer Science Faculty Publications

Presentation slides generated from original research papers provide an efficient form to present research innovations. Manually generating presentation slides is labor-intensive. We propose a method to automatically generates slides for scientific articles based on a corpus of 5000 paper-slide pairs compiled from conference proceedings websites. The sentence labeling module of our method is based on SummaRuNNer, a neural sequence model for extractive summarization. Instead of ranking sentences based on semantic similarities in the whole document, our algorithm measures the importance and novelty of sentences by combining semantic and lexical features within a sentence window. Our method outperforms several baseline methods …

Go to article

Fmri Feature Extraction Model For Adhd Classification Using Convolutional Neural Network, Senuri De Silva, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, Sampath Jayarathna Jan 2021

Fmri Feature Extraction Model For Adhd Classification Using Convolutional Neural Network, Senuri De Silva, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, Sampath Jayarathna

Computer Science Faculty Publications

Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to …

Go to article

Vehicular Crowdsourcing For Congestion Support In Smart Cities, Stephan Olariu Jan 2021

Vehicular Crowdsourcing For Congestion Support In Smart Cities, Stephan Olariu

Computer Science Faculty Publications

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various …

Go to article

Combining Cryo-Em Density Map And Residue Contact For Protein Secondary Structure Topologies, Maytha Alshammari, Jing He Jan 2021

Combining Cryo-Em Density Map And Residue Contact For Protein Secondary Structure Topologies, Maytha Alshammari, Jing He

Computer Science Faculty Publications

Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set …

Go to article

Just-In-Time Pastureland Trait Estimation For Silage Optimization, Under Limited Data Constraints, Patricia O'Byrne Jan 2021

Just-In-Time Pastureland Trait Estimation For Silage Optimization, Under Limited Data Constraints, Patricia O'Byrne

Doctoral

To ensure that pasture-based farming meets production and environmental targets for a growing population under increasing resource constraints, producers need to know pastureland traits. Current proximal pastureland trait prediction methods largely rely on vegetation indices to determine biomass and moisture content. The development of new techniques relies on the challenging task of collecting labelled pastureland data, leading to small datasets. Classical computer vision has already been applied to weed identification and recognition of fruit blemishes using morphological features, but machine learning algorithms can parameterise models without the provision of explicit features, and deep learning can extract even more abstract knowledge …

Go to article

Agile Software Development: Creating A Cost Of Delay Framework For Air Force Software Factories, J. Goljan, Jonathan D. Ritschel, Scott Drylie, Edward D. White Jan 2021

Agile Software Development: Creating A Cost Of Delay Framework For Air Force Software Factories, J. Goljan, Jonathan D. Ritschel, Scott Drylie, Edward D. White

Faculty Publications

The Air Force software development environment is experiencing a paradigm shift. The 2019 Defense Innovation Board concluded that speed and cycle time must become the most important software metrics if the US military is to maintain its advantage over adversaries.1 This article proposes utilizing a cost-o­f-d­elay (CoD) framework to prioritize projects toward optimizing readiness. Cost-­of-d­elay is defined as the economic impact resulting from a delaying product delivery or, said another way, opportunity cost. In principle, CoD assesses the negative impacts resulting from changes to the priority of a project.

Go to article

Multimodal Neuroscience Data Modeling And Inference, Sima Azizi Jan 2021

Multimodal Neuroscience Data Modeling And Inference, Sima Azizi

Doctoral Dissertations

“Mathematical models can be combined with deep learning and machine learning methods to provide new insights in neuroscience. The field of neuroscience is characterized by rich datasets that include fluid biomarkers, EEG signals, and advanced neuroimages. Recent advances in natural language processing have led to the opportunity to gain additional insights from rapidly growing text databases as well as electronic health records. In this research, we focus on applying computational intelligence methods to the analysis of three different complex data sources: blood levels of disease biomarkers, EEG signals from schizophrenic patients, and disease phenotypes encoded in electronic health records. First, …

Go to article

Data And Resource Management In Wireless Networks Via Data Compression, Gps-Free Dissemination, And Learning, Xiaofei Cao Jan 2021

Data And Resource Management In Wireless Networks Via Data Compression, Gps-Free Dissemination, And Learning, Xiaofei Cao

Doctoral Dissertations

“This research proposes several innovative approaches to collect data efficiently from large scale WSNs. First, a Z-compression algorithm has been proposed which exploits the temporal locality of the multi-dimensional sensing data and adapts the Z-order encoding algorithm to map multi-dimensional data to a one-dimensional data stream. The extended version of Z-compression adapts itself to working in low power WSNs running under low power listening (LPL) mode, and comprehensively analyzes its performance compressing both real-world and synthetic datasets. Second, it proposed an efficient geospatial based data collection scheme for IoTs that reduces redundant rebroadcast of up to 95% by only collecting …

Go to article

Efficient Algorithms For Identifying Loop Formation And Computing Θ Value For Solving Minimum Cost Flow Network Problems, Timothy Michael Chávez, Duc Thai Nguyen Jan 2021

Efficient Algorithms For Identifying Loop Formation And Computing Θ Value For Solving Minimum Cost Flow Network Problems, Timothy Michael Chávez, Duc Thai Nguyen

Computational Modeling & Simulation Engineering Faculty Publications

While the minimum cost flow (MCF) problems have been well documented in many publications, due to its broad applications, little or no effort have been devoted to explaining the algorithms for identifying loop formation and computing the value needed to solve MCF network problems. This paper proposes efficient algorithms, and MATLAB computer implementation, for solving MCF problems. Several academic and real-life network problems have been solved to validate the proposed algorithms; the numerical results obtained by the developed MCF code have been compared and matched with the built-in MATLAB function Linprog() (Simplex algorithm) for further validation.

Go to article

Files Cryptography Based On One-Time Pad Algorithm, Ahmad Mohamad Al-Smadi, Ahmad Al-Smadi, Roba Mahmoud Ali Aloglah, Nisrein Abu-Darwish, Ahed Abugabah Jan 2021

Files Cryptography Based On One-Time Pad Algorithm, Ahmad Mohamad Al-Smadi, Ahmad Al-Smadi, Roba Mahmoud Ali Aloglah, Nisrein Abu-Darwish, Ahed Abugabah

All Works

The Vernam-cipher is known as a one-time pad of algorithm that is an unbreakable algorithm because it uses a typically random key equal to the length of data to be coded, and a component of the text is encrypted with an element of the encryption key. In this paper, we propose a novel technique to overcome the obstacles that hinder the use of the Vernam algorithm. First, the Vernam and advance encryption standard AES algorithms are used to encrypt the data as well as to hide the encryption key; Second, a password is placed on the file because of the …

Go to article

Variational Autoencoders And Wasserstein Generative Adversarial Networks For Improving The Anti-Money Laundering Process, Zhiyuan Chen, Waleed Soliman, Amril Nazir, Mohammad Shorfuzzaman Jan 2021

Variational Autoencoders And Wasserstein Generative Adversarial Networks For Improving The Anti-Money Laundering Process, Zhiyuan Chen, Waleed Soliman, Amril Nazir, Mohammad Shorfuzzaman

All Works

There has been much recent work on fraud and Anti Money Laundering (AML) detection using machine learning techniques. However, most algorithms are based on supervised techniques. Studies show that supervised techniques often have the limitation of not adapting well to new irregular fraud patterns when the dataset is highly imbalanced. Instead, unsupervised learning can have a better capability to find anomalous and irregular patterns in new transaction. Despite this, unsupervised techniques also have the disadvantage of not being able to give state-of-the-art detection results. We propose a suite of unsupervised and deep learning techniques to implement an anti-money laundering and …

Go to article

Automatic Fall Risk Detection Based On Imbalanced Data, Yen-Hung Liu, Patrick C. K. Hung, Farkhund Iqbal, Benjamin C. M. Fung Jan 2021

Automatic Fall Risk Detection Based On Imbalanced Data, Yen-Hung Liu, Patrick C. K. Hung, Farkhund Iqbal, Benjamin C. M. Fung

All Works

In recent years, the declining birthrate and aging population have gradually brought countries into an ageing society. Regarding accidents that occur amongst the elderly, falls are an essential problem that quickly causes indirect physical loss. In this paper, we propose a pose estimation-based fall detection algorithm to detect fall risks. We use body ratio, acceleration and deflection as key features instead of using the body keypoints coordinates. Since fall data is rare in real-world situations, we train and evaluate our approach in a highly imbalanced data setting. We assess not only different imbalanced data handling methods but also different machine …

Go to article

Rfid Adaption In Healthcare Organizations: An Integrative Framework, Ahed Abugabah, Louis Sanzogni, Luke Houghton, Ahmad Ali Alzubi, Alaa Abuqabbeh Jan 2021

Rfid Adaption In Healthcare Organizations: An Integrative Framework, Ahed Abugabah, Louis Sanzogni, Luke Houghton, Ahmad Ali Alzubi, Alaa Abuqabbeh

All Works

Radio frequency identification (RFID), also known as electronic label technology, is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteristics using radio frequency signals. Medical equipment information management is an important part of the construction of a modern hospital, as it is linked to the degree of diagnosis and care, as well as the hospital's benefits and growth. The aim of this study is to create an integrated view of a theoretical framework to identify factors that influence RFID adoption in healthcare, as well as to conduct an empirical review of the …

Go to article

The Application Of Machine Learning In Analyzing Organic Compounds From Nmr Spectral Data, Nicole Maia Powell Jan 2021

The Application Of Machine Learning In Analyzing Organic Compounds From Nmr Spectral Data, Nicole Maia Powell

Senior Independent Study Theses

Nuclear magnetic resonance (NMR) is used in organic chemistry to identify unknown organic compounds. The data obtained from an NMR spectrometer are typically shown in the form of a spectrum, which is then analyzed by an analytical chemist. The action of analyzing a spectrum, especially one of a large and complex molecule, is a long and tedious process. In this project, Python is used to implement hierarchical clustering on NMR data obtained from an NMR spectrometer at the College of Wooster to explore its application in NMR analysis. MATLAB is used to build a decision tree from the same data, …

Go to article

Identifying Football Conflict Using Soft-Set Theory In Indonesia Super League, Kukuh Wahyudin Pratama Jan 2021

Identifying Football Conflict Using Soft-Set Theory In Indonesia Super League, Kukuh Wahyudin Pratama

Student Works (2020-2029)

There are several mathematical formal models that handle conflict situations and the most popular one is a rough set theory. With the ability to handle vagueness from the conflict data set, rough set theory has been successfully used in many research. This research used an alternative approach as a method to handle conflict situation in Indonesia Super League. This method was implemented on the respondents or agents who were involved with football club management, match inspector, organizing committee, referees, supporters and players. The novelty of the proposed approach is discussed in rough set theory that include decision rules. It is …

Go to article

Predicting Material Properties: Applications Of Multi-Scale Multiphysics Numerical Modeling To Transport Problems In Biochemical Systems And Chemical Process Engineering, Tom Pace Jan 2021

Predicting Material Properties: Applications Of Multi-Scale Multiphysics Numerical Modeling To Transport Problems In Biochemical Systems And Chemical Process Engineering, Tom Pace

Theses and Dissertations--Physics and Astronomy

Material properties are used in a wide variety of theoretical models of material behavior. Descriptive properties quantify the nature, structure, or composition of the material. Behavioral properties quantify the response of the material to an imposed condition. The central question of this work concerns the prediction of behavioral properties from previously determined descriptive properties through hierarchical multi-scale, multiphysics models implemented as numerical simulations. Applications covered focus on mass transport models, including sequential enzyme-catalyzed reactions in systems biology, and an industrial chemical process in a common reaction medium.

Go to article

First Prev Next Last