Search | Preprints.org

Preprint ARTICLE | doi:10.20944/preprints202101.0472.v1

Surveillance of U.S. Corporate Filings Provides a Proactive Approach to Inform Tobacco Regulatory Research Strategy

S. Emma Sarles, Edward C. Hensel, Risa J. Robinson

Subject: Engineering, Automotive Engineering Keywords: public health; decision-making; evidence; knowledge; translation; Tobacco Regulatory Science; Tobacco Regulation; ecigarette; e-cig; vape; Juul

Online: 25 January 2021 (10:26:40 CET)

Show abstract| Download PDF| Share

The popularity of electronic cigarettes in the United States and around the world has led to a startling rise in youth nicotine use. The Juul e-cigarette was introduced in the U.S. market in 2015 and had captured approximately 13% of the U.S. market by 2017. Unlike many other contemporary electronic cigarette companies, the founders behind the Juul® e-cigarette approached their product launch like a traditional high-tech start-up company, not like a tobacco company. This article presents a case study of Juul’s corporate and product development history in the context of US regulatory actions. The objective of this article is to demonstrate the value of government-curated archives as leading indicators which can (a) provide insight into emergent technologies and (b) inform emergent regulatory science research questions. A variety of sources were used to gather data about the Juul e-cigarette and the corporations that surround it. Sources included government agencies, published academic literature, non-profit organizations, corporate and retail websites, and the popular press. Data was disambiguated, authenticated, and categorized prior to being placed on a timeline of events. A timeline of four significant milestones, nineteen corporate filings and events, twelve US regulatory actions, sixty four patent applications, eighty seven trademark applications, twenty three design patents and thirty two utility patents related to Juul Labs and its associates is presented spanning the years 2004 thought 2020. This work demonstrates the probative value of findings from patent, trademark, and SEC filing literature in establishing a premise for emergent regulatory science research questions which may not yet be supported by traditional archival research literature. The methods presented here can be used to identify key aspects of emerging technologies before products actually enter the market, this shifting policy formulation and problem identification from a paradigm of being reactive in favor of becoming proactive. Such a proactive approach may permit anticipatory regulatory science research and ultimately shorten the elapsed time between market technology innovation and regulatory response.

Preprint REVIEW | doi:10.20944/preprints201911.0278.v1

Machine Learning for Clinical Decision-Making: Challenges and Opportunities

Sergio Sanchez-Martinez, Oscar Camara, Gemma Piella, Maja Cikes, Miguel Angel Gonzalez Ballester, Marius Miron, Alfredo Vellido, Emilia Gomez, Alan Fraser, Bart Bijnens

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Machine learning; clinical decision-making; personalized medicine; digital health

Online: 24 November 2019 (13:26:16 CET)

Show abstract| Download PDF| Share

Preprint CONCEPT PAPER | doi:10.20944/preprints202001.0339.v1

Choices We Make: Rethinking Decision-making in the Context of Climate Crisis

Patrick Waeber, Natasha Stoudmann, Jaboury Ghazoul, Lucienne Wilmé, Jeffrey Sayer, Carlos Nobre, John Innes, Claude Garcia

Subject: Social Sciences, Decision Sciences Keywords: decision-making; change; behavior; climate change; deforestation; social norms; lobbyist, climate denier

Online: 28 January 2020 (10:44:27 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201710.0133.v3

Comparison of Stochastic and Machine Learning Methods for Multi-Step Ahead Forecasting of Hydrological Processes

Georgia Papacharalampous, Hristos Tyralis, Demetris Koutsoyiannis

Subject: Engineering, Civil Engineering Keywords: multi-step ahead forecasting; neural networks; random forests; stochastic vs machine learning models; support vector machines; time series

Online: 17 January 2019 (15:51:22 CET)

Show abstract| Download PDF| Share

Preprint REVIEW | doi:10.20944/preprints202005.0050.v1

The Public Mental Health While in a Community Quarantine Due to COVID-19 Pandemic: A Scoping Review of Literature Using Google Scholar

Ryan Labana

Subject: Social Sciences, Psychology Keywords: COVID-19 Pandemic; Review Literature; Psychosocial Support System; Public Health Administration

Online: 5 May 2020 (02:23:47 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202012.0714.v1

Towards a Biomanufactory on Mars

Aaron Berliner, Jacob M. Hilzinger, Anthony J. Abel, Matthew McNulty, George Makrygiorgos, Nils J.H. Averesch, Soumyajit Sen Gupta, Alexander Benvenuti, Daniel Caddell, Stefano Cestellos-Blanco, Anna Doloman, Skyler Friedline, Desiree Ho, Wenyu Gu, Avery Hill, Paul Kusuma, Isaac Lipsky, Mia Mirkovic, Jorge Meraz, Vince Pane, Kyle B. Sander, Fengzhe Shi, Jeffrey M. Skerker, Alexander Styer, Kyle Valgardson, Kelly Wetmore, Sung-Geun Woo, Yongao Xiong, Kevin Yates, Cindy Zhang, Shuyang Zhen, Bruce Bugbee, Devin Coleman-Derr, Ali Mesbah, Somen Nandi, Robert W. Waymouth, Peidong Yang, Craig S. Criddle, Karen A. McDonald, Amor A. Menezes, Lance C. Seefeldt, Douglas S. Clark, Adam P. Arkin

Subject: Engineering, Automotive Engineering Keywords: space systems bioengineering; human exploration; mars; in situ resource utilization; life support systems; biomanufacturing

Online: 29 December 2020 (07:57:37 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202002.0197.v1

Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus

Rafidah Md Noor, Ng Seong Yik, Raenu Kolandaisamy, Ismail Ahmedy, Mohammad Asif Hossain, Kok-Lim Alvin Yau, Wahidah Md Shah, Tarak Nandy

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Support Vector Machine; Support Vector Regression; Machine learning; Prediction; Urban Smart Bus

Online: 15 February 2020 (14:33:23 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202207.0185.v1

On the Advantages and Disadvantages of Choice

Costanza Scaffidi Abbate, Silvana Miceli

Subject: Business, Economics And Management, Marketing Keywords: choice; decision making; consumer behavior

Online: 12 July 2022 (09:30:20 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202007.0745.v1

Early Detection of The Advanced Persistent Threats Attacks Using Performance Analysis of Deep Learning

Javad Hassannataj Joloudari, Mojtaba Haderbadi, Amir Mashmool, Mohammad GhasemiGol, Shahaboddin Shamshirband, Amir Mosavi

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: APT-attack; detection and classification; feature extraction; machine learning; C5.0 decision tree; Bayesian network; deep learning

Online: 31 July 2020 (11:01:59 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201608.0049.v2

Understanding Farmers’ Decision Making in Agricultural Water Fee Payment in China: The Role of Mental Accounting

Weikang Zhang, Xinhong Fu, Jing Lu, Lin Zhang, Kwamega Michael, Guoqiang Liu, Yuying Liu, Fan Yang

Subject: Business, Economics And Management, Economics Keywords: mental accounting; agricultural water fee; behavioral economics; decision making; information processing; representativeness; negative psychological externalities

Online: 25 August 2016 (10:12:08 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202012.0221.v1

Building a Laboratory at a Primarily Undergraduate Institution (PUI)

Caroline Dahlberg, Christina King-Smith, Blake Riggs

Subject: Social Sciences, Education Keywords: PUI (primarily undergraduate institution); laboratory; start-up; negotiation; undergraduate; support; funding; equipment; mentorship; community

Online: 9 December 2020 (11:16:48 CET)

Show abstract| Download PDF| Share

Scientists who are interested in building research programs at primarily-undergraduate institutions (PUIs) have unique considerations compared to colleagues at research-intensive (R1) institutions. Maintaining a research program at a PUI holds unique challenges that should be considered before prospective faculty go on the job market, as they negotiate a job offer, and after they begin a new position. In this article we describe some of the considerations that aspiring and newly hired faculty should keep in mind as they plan out how they will set up a laboratory as a new Principle Investigator (PI) at a PUI. Anyone hoping to start a research program at a PUI should understand both the timeframe of interviews, job offers, and negotiations and the challenges and rewards of working with undergraduate researchers. Once a job is offered, candidates should be aware of the range of negotiable terms that can be part of a start-up package. Space and equipment considerations are also important, and making the most of shared spaces, existing infrastructure, and deals can extend the purchasing power of start-up funds as a new PIs builds their lab. PUIs’ focus on undergraduate education and mentorship leads to important opportunities for collaboration, funding, and bringing research projects directly into undergraduate teaching laboratories. A major focus of any new laboratory leader must be on building a productive, equitable, and supportive laboratory community. Equitable onboarding, mentorship plans, and formalized expectations, can all help build a productive and sustainable laboratory research program. However, important considerations about safety, inclusion, student schedules, and a PI’s own professional commitments are also extremely important concerns when working with undergraduates in research. A successful research program at a PUI will bring students into meaningful scientific inquiry and requires insights and skills that are often not the focus of scientific training. This article aims to describe the scope of setting up a new laboratory as a way to alleviate some of the burden that new and prospective faculty often feel.

Preprint REVIEW | doi:10.20944/preprints202103.0216.v1

Machine Learning: Algorithms, Real-World Applications and Research Directions

Iqbal H. Sarker

Subject: Computer Science And Mathematics, Information Systems Keywords: machine learning; deep learning; artificial intelligence; data science; data-driven decision making; predictive analytics; intelligent applications;

Online: 8 March 2021 (12:55:59 CET)

Show abstract| Download PDF| Share

Preprint REVIEW | doi:10.20944/preprints201705.0104.v1

A Systematic Review: Family Support Integrated To Diabetes Self-Management among Glycemic Uncontrolled Type II DM Patients

Rian Adi Pamungkas, Kanittha Chamroonsawasdi, Paranee Vatanasomcoon

Subject: Social Sciences, Behavior Sciences Keywords: diabetes self-management; family support; glycemic uncontrolled; type 2 DM; systematic review

Online: 12 May 2017 (05:27:28 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202101.0013.v1

Social Support and Emotional Maturity to Reduce Students’ Online Learning Anxiety During the Covid-19 Pandemic

Andik Matulessy, Sahat Saragih, Nawafil Nawafil, Hengki Perdana, Moses Pandin

Subject: Social Sciences, Education Keywords: Social support; emotional maturity; anxiety; online learning

Online: 4 January 2021 (11:26:27 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201609.0119.v1

Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network

Cheng-Ming Lee, Chia-Nan Ko

Subject: Engineering, Electrical And Electronic Engineering Keywords: short-term load forecasting; radial basis function neural network; support vector regression; particle swarm optimization; adaptive annealing learning algorithm

Online: 29 September 2016 (12:22:20 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201706.0093.v2

The Open Energy Modelling Framework (oemof) - A New Approach to Facilitate Open Science in Energy System Modelling

Simon Hilpert, Cord Kaldemeyer, Uwe Krien, Stephan Günther, Clemens Wingenbach, Guido Plessmann

Subject: Engineering, Energy And Fuel Technology Keywords: decision support; energy system modelling; optimization; collaborative development; open science

Online: 27 March 2018 (05:34:38 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201609.0076.v1

Evaluation of Cloud Services: A Fuzzy Multicriteria Group Decision Making Method

Santoso Wibowo, Hepu Deng, Wei Xu

Subject: Computer Science And Mathematics, Analysis Keywords: performance evaluation; cloud services; group decision making; multicriteria decision making; fuzzy sets

Online: 22 September 2016 (10:45:55 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201609.0104.v1

Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

Tiannan Ma, Dongxiao Niu

Subject: Computer Science And Mathematics, Computer Science Keywords: icing forecasting; fireworks algorithm; least square support vector machine; feature selection

Online: 27 September 2016 (11:16:44 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202103.0455.v1

Implementation of TQM and the Integration of BIM in the Construction Management Sector in Saudi Arabia Validated with Hybridized Emerging Harris Hawks Optimization (HHO)

Mohammad Abazid, Hüseyin Gökçekuş, Tahir Celik

Subject: Engineering, Automotive Engineering Keywords: Total Quality Management; construction projects; Saudi Arabia; Harris-hawks optimization; support vector regression

Online: 17 March 2021 (17:01:02 CET)

Show abstract| Download PDF| Share

Preprint CONCEPT PAPER | doi:10.20944/preprints201810.0773.v2

A Comparative Study of the AHP and TOPSIS Techniques for Dam Site Selection Using GIS: A Case Study of Sistan and Baluchestan Province, Iran

Ali Jozaghi, Babak Alizadeh, Mohsen Hatami, Ian Flood, Mohammad Khorrami, Nastaran Khodaei, Erfan Ghasemi Tousi

Subject: Engineering, Civil Engineering Keywords: multi-criteria decision-making; TOPSIS; AHP; water resources; dam site selection

Online: 18 December 2018 (12:04:57 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201611.0056.v1

Clinical Decision-Making in the Treatment of Schizophrenia: Focus on Long-Acting Injectable Antipsychotics

Ludovic Samalin, Marion Garnier, Candy Auclair, Pierre-Michel Llorca

Subject: Medicine And Pharmacology, Psychiatry And Mental Health Keywords: long-acting injectable; antipsychotic; decision-making; guidelines; schizophrenia

Online: 10 November 2016 (07:00:52 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201708.0055.v1

A Survey of Data Processing of EMR (Electronic Medical Record) Based on Data Mining

Wencheng Sun, Fang Liu, Zhiping Cai, Shengqun Fang, Guoyan Wang

Subject: Computer Science And Mathematics, Information Systems Keywords: EMR; data preprocessing; text mining; information extraction; medical decision support system

Online: 15 August 2017 (05:46:43 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201912.0154.v1

Weighting the Key Features Affecting Supplier Selection using Machine Learning Techniques

Ahmad Abdulla, George Baryannis, Ibrahim Badi

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: supplier selection; machine learning; decision tree

Online: 11 December 2019 (12:21:26 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201612.0078.v1

An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data

Yuzhe Liu, Vanathi Gopalakrishnan

Subject: Computer Science And Mathematics, Computer Science Keywords: missing value imputation; machine learning; decision tree imputation; k-nearest neighbors imputation; self-organizing map imputation

Online: 15 December 2016 (08:27:13 CET)

Show abstract| Download PDF| Supplementary Files| Share

Preprint ARTICLE | doi:10.20944/preprints201703.0002.v1

Iterated Economic Games, Logic Gates, and the Flow of Shared Information in Strategic Interactions

Michael Harré

Subject: Business, Economics And Management, Economics Keywords: information theory; transfer entropy; game theory; logic gates; multilayer perceptrons; strategic behaviour; decision theory

Online: 1 March 2017 (09:26:28 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201702.0102.v1

Experimental Studies of Turbulence Intensity around a Tidal Turbine Support Structure

Stuart Walker, Lorenzo Cappietti

Subject: Engineering, Energy And Fuel Technology Keywords: Marine Energy; Tidal turbine; horizontal-axis marine current turbine; OCT; Turbulence; Support Structure; device wake

Online: 28 February 2017 (11:55:00 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201711.0163.v1

A Hybrid MCDM Technique for Risk Management in Construction Projects

Kajal Chatterjee, Edmundas Kazimieras Zavadskas, Jolanta Tamošaitienė, Krishnendu Adhikary, Samarjit Kar

Subject: Engineering, Civil Engineering Keywords: D number, Analytical Network Process (ANP), MABAC, Multi-criteria decision making (MCDM), Consistent fuzzy preference relation (CFPR), Construction project risk, Risk Management

Online: 24 November 2017 (16:46:59 CET)

Show abstract| Download PDF| Share

Preprint REVIEW | doi:10.20944/preprints202203.0087.v1

Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions

Iqbal H. Sarker, Asif Irshad Khan, Yoosef B. Abushark, Fawaz Alsolami

Subject: Computer Science And Mathematics, Security Systems Keywords: Internet of Things; cyber-attacks; anomalies; machine learning; deep learning; IoT data analytics; intelligent decision-making; security intelligence

Online: 7 March 2022 (02:39:58 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202008.0415.v1

Monitoring Coronavirus COVID-19/SARS-CoV-2 Pandemic using GIS Dashboard: International and Indonesia Context

Edy Irwansyah, Widodo Budiharto, Danu Widhyatmoko, Achmad Istamar, Fepri Putra Panghurian

Subject: Medicine And Pharmacology, Other Keywords: pandemic; covid-19; monitoring; GIS dashboard; emergency spatial support centre

Online: 19 August 2020 (11:48:55 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201702.0077.v1

Shape and Geometric Features-based Semantic Image Retrieval Using Multi-class Support Vector Machine

Zohair Malki

Subject: Computer Science And Mathematics, Computer Science Keywords: Zernike moment, Multi-class support vector machine, Query Engine, SPARQL

Online: 20 February 2017 (18:07:11 CET)

Show abstract| Download PDF| Share

Preprint REVIEW | doi:10.20944/preprints202108.0521.v1

The Costs and Potential Benefits of Introducing the “I Don’t Know” Answer in Binary Classification Settings

Damjan Krstajic

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: non-applicability domain; binary classification; ignorance; decision-making

Online: 27 August 2021 (13:05:02 CEST)

Show abstract| Download PDF| Share

Preprint REVIEW | doi:10.20944/preprints201909.0233.v1

The Evolution of Grooming and Hand Use in Primates: An Interdisciplinary Perspective

Alexander Dunkel

Subject: Biology And Life Sciences, Ecology, Evolution, Behavior And Systematics Keywords: primate hand use; primate grooming; manual grooming; object manipulation; primate evolution; oral grooming; object play; tool use; Machiavellian Intelligence; Bayesian decision theory

Online: 20 September 2019 (06:39:59 CEST)

Show abstract| Download PDF| Share

Preprint REVIEW | doi:10.20944/preprints202006.0139.v1

Cybersecurity Data Science: An Overview from Machine Learning Perspective

Iqbal H. Sarker

Subject: Computer Science And Mathematics, Security Systems Keywords: Cybersecurity; machine learning; data science; decision making; cyber-attack; security modeling; intrusion detection; threat intelligence

Online: 11 June 2020 (12:12:50 CEST)

Show abstract| Download PDF| Share

Preprint COMMUNICATION | doi:10.20944/preprints202207.0329.v1

Biomanufacturing for Space-Exploration – What to Take and When to Make

Nils J.H. Averesch, Aaron J. Berliner, Shannon N. Nangle, Spencer Zezulka, Gretchen L. Vengerova, Davian Ho, Cameran A. Casale, Benjamin A.E. Lehner, Jessica E. Jessica Snyder, Kevin B. Clark, Lewis R. Dartnell, Craig S. Criddle, Adam P. Arkin

Subject: Engineering, Bioengineering Keywords: in-space biomanufacturing; space synthetic biology; space bioprocess engineering; biological life-support systems; in situ resource utilization; circular economy

Online: 21 July 2022 (11:01:29 CEST)

Show abstract| Download PDF| Supplementary Files| Share

Preprint ARTICLE | doi:10.20944/preprints201809.0279.v2

Social Plasticity in the Fish Brain: Neuroscientific and Ethological Aspects

Karen Maruska, Marta C. Soares, Monica Lima-Maximino, Diógenes Henrique de Siqueira-Silva, Caio Maximino

Subject: Social Sciences, Behavior Sciences Keywords: brain plasticity; cichlids; cleanerfish; social plasticity; social decision making network; zebrafish

Online: 17 January 2019 (03:48:04 CET)

Show abstract| Download PDF| Share

Preprint REVIEW | doi:10.20944/preprints201912.0276.v1

The Future of Emerging IoT Paradigms: Architectures and Technologies

Abdul Salam, Anh Duy Hoang, Atluri Meghna, Dylan R. Martin, Gabriel Guzman, Yung Han Yoon, Jacob Carlson, Jordan Kramer, Keim Yansi, Michael Kelly, Michael Skvarek, Milos Stankovic, Nguyen Dang Khoa Le, Tyler Wierzbicki, Xiaozhe Fan

Subject: Engineering, Control And Systems Engineering Keywords: IoT in Healthcare; IoT in Vehicular Networks; Behaviors and Decision Making, IoT in Learning Environments; IoT in Mining; Io IoT in Energy Systems; IoT in Smart Cities; Sensors; Low Power Networks

Online: 20 December 2019 (12:37:09 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201705.0124.v1

Walking across rural-urban fringe: the hindering effect of intersections

Ivan Blecic, Tanja Congiu, Dario Canu, Giovanna Fancello

Subject: Social Sciences, Urban Studies And Planning Keywords: rural-urban fringe; walkability; road intersections; decision support methods; Electre Tri

Online: 16 May 2017 (13:50:46 CEST)

Show abstract| Download PDF| Share

Preprint REVIEW | doi:10.20944/preprints202009.0086.v1

Molecular Pharming to Support Human Life on the Moon, Mars, and Beyond

Matthew J. McNulty, Yongao Xiong, Kevin Yates, Kalimuthu Karuppanan, Jacob M. Hilzinger, Aaron J. Berliner, Jesse Delzio, Adam P. Arkin, Nancy E. Lane, Somen Nandi, Karen A. McDonald

Subject: Biology And Life Sciences, Biology And Biotechnology Keywords: life support for space; plant molecular pharming

Online: 4 September 2020 (07:31:34 CEST)

Show abstract| Download PDF| Supplementary Files| Share

Preprint ARTICLE | doi:10.20944/preprints201707.0097.v1

The Influence of Structural Conditions and Cultural Inertia on Water Usage and Landscape Decision-Making in a California Metropolitan Area

Madhusudan Katti, Andrew R. Jones, Derya Özgöç Çağlar, Henry D. Delcore, Kaberi Kar Gupta

Subject: Social Sciences, Urban Studies And Planning Keywords: urban sustainability; California; landscape decision-making; urban environment; water use behaviors; social-ecological systems

Online: 31 July 2017 (16:45:57 CEST)

Show abstract| Download PDF| Supplementary Files| Share

Preprint REVIEW | doi:10.20944/preprints201705.0129.v1

Phenotypic Plasticity and Cell Fate Decisions in Cancer: Insights from Dynamical Systems Theory

Dongya Jia, Mohit Kumar Jolly, Prakash Kulkarni, Herbert Levine

Subject: Biology And Life Sciences, Biophysics Keywords: cell fate decision; cancer attractors; gene network dynamics; EMT; drug resistance; intrinsically disordered proteins

Online: 17 May 2017 (06:25:17 CEST)

Show abstract| Download PDF| Share

Waddington’s epigenetic landscape, a famous metaphor in developmental biology, depicts how a stem cell progresses from an undifferentiated phenotype to a differentiated one. The concept of “landscape” in the context of dynamical system theory represents a high-dimensional cell state space, in which each cell phenotype is considered as an “attractor” that is determined by interactions among multiple variables (molecular players), and is buffered against environmental fluctuations. Further, biological noise is thought to play an important role during these cell-fate decisions and in fact controls transitions between different phenotypes. Here, we discuss these phenotypic transitions in cancer from a dynamical systems perspective and invoke the concept of “cancer attractors” – hidden stable states of the underlying regulatory network that are not occupied by normal cells. Using epithelial-to-mesenchymal transition (EMT), cancer stem-like properties, metabolic reprogramming and the emergence of drug/hormone resistance as examples, we illustrate how phenotypic plasticity in cancer cells enables them to acquire hybrid phenotypes (such as hybrid epithelial/mesenchymal and hybrid metabolic phenotypes) that tend to be more aggressive and notoriously resilient to drug/hormone treatment. Furthermore, we highlight multiple factors that may give rise to phenotypic plasticity in cancer cells, such as (a) multi-stability or oscillatory behaviors governed by underlying regulatory networks involved in cell-fate decisions in cancer cells, and (b) network rewiring due to conformational dynamics of intrinsically disordered proteins (IDPs) that are highly enriched in cancer cells. We conclude by discussing why a therapeutic approach that promotes ‘recanalization’, i.e. the exit from “cancer attractors” and re-entry into “normal attractors”, is more likely to succeed rather than a conventional approach that targets individual molecules/pathways.

Preprint ARTICLE | doi:10.20944/preprints201707.0015.v2

Multiple Attribute Decision Making (MADM) in Proportional-Stop-Loss Reinsurance

Jiexuan Wang, Kim Leng Poh

Subject: Engineering, Industrial And Manufacturing Engineering Keywords: multi-attribute decision making; reinsurance; proportional reinsurance; non-proportional reinsurance; TOPSIS

Online: 10 July 2017 (15:42:50 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202101.0363.v1

Extension of Equivalent System Mass for Human Exploration Missions on Mars

Aaron Berliner, George Makrygiorgos, Avery Hill

Subject: Engineering, Automotive Engineering Keywords: Space Systems Bioengineering; Equivalent System Mass; ESM; Environmental Control and Life Support Systems; ECLSS; Space Logistics; Uncertainty; Risk

Online: 18 January 2021 (17:39:26 CET)

Show abstract| Download PDF| Supplementary Files| Share

Preprint ARTICLE | doi:10.20944/preprints201706.0094.v3

The Cue-Approach Task as a General Mechanism for Long-Term Non-Reinforced Behavioral Change

Tom Salomon, Rotem Botvinik-Nezer, Tony Gutentag, Rani Gera, Roni Iwanir, Maya Tamir, Tom Schonberg

Subject: Social Sciences, Cognitive Science Keywords: cue-approach; decision making; behavioral change; preferences; emotion

Online: 23 October 2017 (03:42:21 CEST)

Show abstract| Download PDF| Supplementary Files| Share

Preprint ARTICLE | doi:10.20944/preprints201811.0530.v2

Digital Image Segmentation in Matlab: A Brief Study on Otsu’s Image Thresholding

Md. Abu Bakr Siddique, Rezoana Bente Arif, Mohammad Mahmudur Rahman Khan

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Image segmentation, Otsu’s thresholding, Two level thresholding, Three level thresholding, Error minimization, Bayes decision, Probability density function (PDF).

Online: 5 February 2019 (08:12:08 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201801.0193.v1

A Multi-Agent System Based Approach to Fight Financial Fraud: An Application to Money Laundering

Claudio Alexandre, João Balsa

Subject: Computer Science And Mathematics, Computer Science Keywords: multi-agent system; decision support; anti-money laundering; anti-fraud

Online: 22 January 2018 (04:46:20 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202003.0087.v1

The Global Forest Transition is a Human Affair

Claude A. Garcia, Sini Savilaakso, René W. Verburg, Victoria Gutierrez, Sarah Jane Wilson, Cornelia B. Krug, Marieke Sassen, Brian E. Robinson, Hannah Moersberger, Babak Naimi, Jeanine M. Rhemtulla, Hélène Dessard, Valéry Gond, Cédric Vermeulen, Franck Trolliet, Johan Oszwald, Fabien Quétier, Stephan A Pietsch, Jean-Francois Bastin, Anne Dray, Jaboury Ghazoul, Patrick O. Waeber

Subject: Environmental And Earth Sciences, Environmental Science Keywords: Forest transition, sustainable transformation, boundary objects, games, decision-making, agency

Online: 5 March 2020 (11:58:55 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201705.0091.v1

The Government Support and the Innovation of the New Energy Firms

Weijian Du, Mengjie Li

Subject: Business, Economics And Management, Economics Keywords: Government support; Innovation probability; Innovation destiny; Propensity score matching

Online: 10 May 2017 (18:04:33 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201804.0314.v1

Application of AHP and DEMATEL Methods in Choosing and Analysing the Measures for the Distribution of Goods in Szczecin Region

Kinga Kijewska, Witold Torbacki, Stanisław Iwan

Subject: Social Sciences, Decision Sciences Keywords: urban freight transport; city logistics; decision making process; multi-actor decision support; Multi-Criteria Decision Analysis; MCDA; Analytic Hierarchy Process; AHP; Decision Making Trial and Evaluation Laboratory Method; DEMATEL

Online: 24 April 2018 (09:19:37 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201801.0125.v1

Cloud Service Providers Optimized Ranking Algorithm Based on Machine Learning and Multi-Criteria Decision Analysis

Muhammad Umer Wasim, Abdallah A. Z. A. Ibrahim, Pascal Bouvry, Tadas Limba

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: multi-criteria decision analysis (MCDA); online broker; misspecification of criteria; structural uncertainty; unsupervised machine learning; factor analysis, quality of service (QoS)

Online: 15 January 2018 (11:29:56 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202003.0298.v1

A Composite Hybrid Feature Selection Learning-Based Optimization of Genetic Algorithm For Breast Cancer Detection

Ahmed Abdullah Farid, Gamal Selim, Hatem Khater

Subject: Computer Science And Mathematics, Information Systems Keywords: Data Mining; Breast Cancer; Hybrid Feature Selection; Machine learning; Support Vector Machine; Optimize Genetic Algorithm; boosting algorithms

Online: 19 March 2020 (11:13:15 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.3390/sci2030060

Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Jian Zhang, Rahul Soangra, Thurmon E. Lockhart

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: locomotion; machine learning; support vector machines; activity classification; activity of daily life (ADL)

Online: 18 July 2020 (00:00:00 CEST)

Show abstract| Share

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter

γ

—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

Preprint ARTICLE | doi:10.20944/preprints201704.0138.v1

The Right to Remember: Implementing a Rudimentary Emotive-Effect Layer for Frustration on AI Agent Gameplay Strategy

James Stallwood, Ashok Ranchhod

Subject: Computer Science And Mathematics, Computer Science Keywords: AI Agents; emotive layer; judgement; behaviour; probability; decision-making; gameplay; backpropagation.

Online: 21 April 2017 (12:07:53 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202004.0266.v1

A Suggestion from South Korea for Treatment of Mild or Asymptomatic COVID-19 Infected Patients: Living and Treatment Support Center

EunKyo Kang

Subject: Biology And Life Sciences, Virology Keywords: COVID-19; mild patients; quarantine facility; video-consultation; living and treatment support center

Online: 16 April 2020 (08:23:06 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201802.0105.v1

TOPSIS Based Algorithm for Solving Multi-objective Multi-level Programming Problem with Fuzzy Parameters

Surapati Pramanik, Partha P. Dey, Florentin Smarandache

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: multi-objective multi-level programming; fuzzy parameters; TOPSIS; fuzzy goal programming; multi-objective decision making

Online: 15 February 2018 (20:29:20 CET)

Show abstract| Download PDF| Share

Working Paper ARTICLE

An Equilibrium Theorem of Strategies in Non-Linear Population Matrix Models

Mark Burgess

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: evolutionary stable strategies (ESS); Markov decision evolutionary games (MDEG); Hawk-Dove game; evolutionary dynamics; evolutionary game theory

Online: 24 June 2021 (08:38:13 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201801.0006.v1

NS-Cross Entropy Based MAGDM under Single Valued Neutrosophic Set Environment

Surapati Pramanik, Shyamal Dalapati, Shariful Alam, F. Smarandache, Tapan Kumar Roy

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: neutrosophic set; single valued neutrosophic set; SN-cross entropy function; multi-attribute group decision making

Online: 2 January 2018 (09:11:21 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201706.0061.v1

Generalized Triangular Intuitionistic Fuzzy Geometric Averaging Operator for Decision Making in Engineering

Daniel O. Aikhuele, Sarah Odofin

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: generalized triangular intuitionistic fuzzy geometric aggregation operator; triangular intuitionistic fuzzy number; intuitionistic fuzzy set; multi-criteria decision-making; attitudinal character; flexibility

Online: 14 June 2017 (05:55:52 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201801.0065.v1

Cross Entropy Measures of Bipolar and Interval Bipolar Neutrosophic Sets and Their Application for Multi-Attribute Decision Making

Surapati Pramanik, Partha Pratim Dey, Florentin Smarandache, Jun Ye

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: neutrosophic set; bipolar neutrosophic set; interval bipolar neutrosophic set; multi-attribute decision making; cross entropy measure

Online: 8 January 2018 (11:04:02 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201708.0104.v1

Comparison and Screening of NFC Options in View of Sustainable Performance and Waste Management

Aleksandra Schwenk-Ferrero, Andrey Andrianov

Subject: Engineering, Energy And Fuel Technology Keywords: advanced nuclear fuel cycles; waste management; resource utilization; economics; performance comparison; multi-criteria decision analysis; sensitivity/uncertainty analysis; environmental footprint

Online: 30 August 2017 (12:35:24 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201710.0127.v1

Revealing ETC-1922159 Affected Unknown 3^rd order WNT10B-X-X Combinations, in Silico

shriprakash sinha

Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: WNT pathway; porcupine inhibitor ETC-1922159; sensitivity analysis; colorectal cancer; unknown biological hypotheses; combinatorial search space; support vector ranking

Online: 18 October 2017 (05:58:17 CEST)

Show abstract| Download PDF| Share

WNT10B belongs to the family of WNT proteins that are implicated in a range of phenomena that are affected by the Wnt signaling pathway. Recent studies have shown that WNT10B plays a role in colorectal cancer. WNTs have been found to directly affect the stemness of the tumor cells via regulation of ASCL2. Switching off the ASCL2 literally blocks the stemness process of the tumor cells and vice versa. Furthermore, recent findings suggest BVES to be highly suppressed in malignancies and in vitro deletions of BVES show higher Wnt signaling activity to induce stem- ness. WNT10B was found to be highly expressed in such cases. Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. For example, WNT10B-ASCL2 is one such 2^ndorder combination whose relation needs to be tested under the influence of recently developed porcupine-WNT inhibitor ETC-1922159. The inhibitor is known to suppress PORCN (porcupine) and thus inhibit a range of oncogenes known to be directly or indirectly affected by the Wnts. In a recent unpublished work in bioRxiv, Sinha 1, we had the opportunity to rank these unknown biological hypotheses for down regulated genes at 2^ndorder level after the drug was administered. The in silico observations showed that the combination of WNT10B-ASCL2 was assigned a relatively lower rank, thus validating the pipeline’s efficacy with the confirmed wet lab experiment that indicate that both WNT10B and ASCL2 were down regulated after treatment in cancer cells. Here, we take one step further by in silico analysis of the 3rd order combinations of WNT10B-X-X (X can be known or unknown factor), from a range of 100 randomly picked down regulated genes after ETC-1922159 treatment. The pipeline uses the density based HSIC (Hilbert Schmidt Information Criterion) sensitivity index with an rbf (ra- dial basis function) kernel, which is known to be highly effective in sensitivity analysis. Various unknown/unexplored/untested 3^rdorder biological hypotheses emerge some of which are con- firmed in wet lab, while others need to be tested. The potential of such ranking is indispensable in the current era of search in a vast combinatorial forest.

Preprint ARTICLE | doi:10.20944/preprints201712.0055.v1

An Empirical Study on Value Relevance of Fair Value: Based on the Data of Financial Industry and Manufacturing Industry in China

Jinyan Wu

Subject: Business, Economics And Management, Accounting And Taxation Keywords: fair value; value-relevance; decision-making useful view; price model

Online: 11 December 2017 (05:25:13 CET)

Show abstract| Download PDF| Share

Working Paper ARTICLE

Cyber Risk at the Edge: Current and future trends on Cyber Risk Analytics and Artificial Intelligence in the Industrial Internet of Things and Industry 4.0 Supply Chains

Petar Radanliev, David De Roure, Kevin Page, Jason R.C. Nurse, Rafael Mantilla Montalvo, Omar Santos, La'Treall Maddox, Peter Burnap

Subject: Engineering, Automotive Engineering Keywords: Industry 4.0; Supply Chain Design; Transformational Design Roadmap; IIoT Supply Chain Model; Decision Support for Information Management

Online: 24 December 2020 (13:37:35 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201803.0042.v1

Improving Decisions to Mitigate the Risks of Organizational Accidents

Scott Jackson, Avi Harel

Subject: Social Sciences, Psychology Keywords: cognitive bias; organizational bias; decision options; risk; catastrophic, organizational accidents; human error; hierarchy; culture; policy; procedures

Online: 6 March 2018 (05:21:28 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201710.0146.v1

NC-TODIM Based MAGDM under Neutrosophic Cubic Set Environment

Surapati Pramanik, Shyamal Dalapati, Shariful Alam, Tapan Kumar Roy

Subject: Computer Science And Mathematics, Mathematics Keywords: neutrosophic cubic set; single valued neutrosophic set; interval neutrosophic set; multi attribute group decision making; TODIM method; NC-TODIM

Online: 23 October 2017 (04:53:57 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201805.0042.v1

Organic Food Products Labeling Innovation: The Case for Sustainable Food Consumption Behavior

Daniel Adrian Gârdan, Ionel Dumitru, Ilić Dragan, Iuliana Petronela Gârdan, Mihai Andronie, Cristian Uță

Subject: Business, Economics And Management, Marketing Keywords: products labeling; organic food products labeling; labeling innovation; sustainable food consumption; buying decision

Online: 2 May 2018 (13:10:20 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201709.0009.v1

Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000–2015 in a Mediterranean Semiarid Area

Francisco Gomariz-Castillo, Francisco Alonso-Sarría, Fulgencio Cánovas-García

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: machine learning; sequential maximum a posteriori; random forest; support vector machines; land use classification; textural information; contextual information

Online: 4 September 2017 (10:43:26 CEST)

Show abstract| Download PDF| Supplementary Files| Share

Preprint ARTICLE | doi:10.20944/preprints202103.0395.v1

Quantum-Like Cognition and Rationality: Biological and Artificial Intelligence Systems

Andrei Khrennikov

Subject: Social Sciences, Psychology Keywords: mathematical formalism; quantum mechanics; quantum instruments; psychological effects; cognition; decision making; classical rationality; quantum rationality; information overload

Online: 15 March 2021 (14:12:53 CET)

Show abstract| Download PDF| Share

Preprint REVIEW | doi:10.20944/preprints202104.0442.v1

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

Iqbal H. Sarker

Subject: Computer Science And Mathematics, Information Systems Keywords: data science; advanced analytics; machine learning; deep learning; smart computing; decision-making; predictive analytics; data science applications;

Online: 16 April 2021 (11:28:09 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202103.0667.v1

The Individual and the Organizational Model of Quantum Decision-Making and Learning (MQDM&L): An Introduction and the application of the Quadruple Loop Learning

Meir Russ

Subject: Business, Economics And Management, Business And Management Keywords: Model of Quantum Decision-Making and Learning (MQDM&L), AADD diamond model, organizational, individual, decision-making, learning, quadruple loop learning, Padkos

Online: 26 March 2021 (14:15:25 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201709.0117.v1

Significance Tests for Binomial Experiments: Ordering the Sample Space by Bayes Factors and Using Adaptive Significance Levels for Decisions

Carlos A. de B. Pereira, Adriano Polpo, Eduardo Y. Nakano

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: significance level; sample size; bayes ratio; likelihood function; optimal decision; significance test

Online: 25 September 2017 (08:33:18 CEST)

Show abstract| Download PDF| Share

Preprint REVIEW | doi:10.20944/preprints201705.0003.v3

Introducing Sustainability in Value Models to Support Design Decision Making: A Systematic Review

Marco Bertoni

Subject: Engineering, Industrial And Manufacturing Engineering Keywords: sustainability; value modeling; decision support; value driven design; review

Online: 12 June 2017 (13:51:25 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201807.0076.v1

Flood Prevention and Emergency Response System Powered by Google Earth Engine

Cheng-Chien Liu, Ming-Chang Shieh, Ming-Hsun Ko, Kung-Hwa Wang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: flood; disaster prevention; emergency response; decision making, Google earth engine

Online: 4 July 2018 (15:33:44 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202102.0195.v1

The Importance of Music Education in the Development and Performance of Executive Functions

Carlos Andrés Domínguez Villamizar

Subject: Social Sciences, Psychology Keywords: Executive functions; music education; cognitive development; planning; inhibition; decision making; working memory

Online: 8 February 2021 (11:44:19 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201907.0014.v1

Synergies and Trade-offs in the Sustainable Development Goals – the Implications of the Icelandic Tourism Sector

David Cook, Nína Saviolidis, Brynhildur Davíðsdóttir, Lára Jóhannsdóttir, Snjólfur Ólafsson

Subject: Social Sciences, Geography, Planning And Development Keywords: decision-making; tourism; sustainable development goals; Iceland; synergies; trade-offs

Online: 1 July 2019 (12:10:11 CEST)

Show abstract| Download PDF| Supplementary Files| Share

The development of major economic sectors can provide the bedrock on which long-lasting national economic prosperity is formed. Iceland’s tourism sector is an example of a rapidly expanded industry in recent years, to the extent that it has become the largest sectoral contributor to the nation’s economy. The growth of the sector has led to a number of sustainability impacts, thus presenting opportunities and challenges in terms of meeting the seventeen Sustainable Development Goals (SDGs) of the United Nations. Using the case study of Iceland, this paper aims to advance conceptual understanding of the synergies and trade-offs between a nation’s tourism sector and performance across the 169 targets of the SDGs. Empirical results were derived from four theme-based focus groups, comprised of expert participants, who were tasked with completing scoresheets concerning their perception of the extent of synergies and trade-offs for each target. The majority (126 in number) of the mean scoresheet outcomes for the SDG targets revealed neither synergies nor trade-offs. However, 32 synergies and 11 trade-offs were identified. Many of the target synergies related to new economic opportunities, such as jobs, employment and training for young people. Target trade-offs tended to be environmental and social. In particular, concern was voiced about the greenhouse gas emissions of the Icelandic tourism sector, which derives from international aviation, cruise ships and rental car usage. The outcomes of this study are of particular relevance to tourism companies, policy-makers and governance institutes, all of whom are increasingly endeavouring to link their activities with the fulfilment of the SDGs, maximising synergies, mitigating the extent of any potential trade-offs, and potentially transforming trade-offs into synergies. Furthermore, the results are likely of interest to academics focused on researching the broad sustainability impacts of economic sectors and their contribution to meeting the visionary goals of the SDGs.

Preprint ARTICLE | doi:10.20944/preprints201711.0033.v1

SRRTC: Social Recommendation Based on Relationship Transmission with Convergence Algorithm

Huakang Li, Yixiong Bian, Xiuying Xu, Guozi Sun

Subject: Computer Science And Mathematics, Information Systems Keywords: social recommendation; relationship graph; micro relation transfer model; macro relation transfer model; monte carlo decision tree

Online: 6 November 2017 (06:36:37 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201710.0032.v1

What do Users Really Want? Participatory Development of Decision Support Tools for Environmental Management based on Outcomes.

Richard J. Hewitt, C.J.A. Macleod

Subject: Social Sciences, Geography, Planning And Development Keywords: Environmental decision support systems; applications; outcome-based approach; adaptive management; user requirements; environmental management; participatory land planning

Online: 6 October 2017 (08:51:07 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201709.0170.v1

Exploring R&D Influences on Financial Performance for Business Sustainability Considering Dual Profitability Objectives

Kao-Yi Shen, Min-Ren Yan, Gwo-Hshiung Tzeng

Subject: Business, Economics And Management, Business And Management Keywords: business sustainability; research and development (R&D); multiple criteria decision-making (MCDM); financial objective; variable-consistency dominance-based rough set approach (VC-DRSA); internetwork relationship map (INRM); directional flow graph (DFG)

Online: 30 September 2017 (06:19:43 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202104.0791.v1

Global Prevalence and Drivers of Dental Students' COVID-19 Vaccine Hesitancy

Abanoub RIAD, Huthaifa ABDULQADER, Mariana MORGADO, Silvi DOMNORI, Michal KOŠČÍK, José João MENDES, Miloslav KLUGAR, Elham KATEEB

Subject: Medicine And Pharmacology, Epidemiology And Infectious Diseases Keywords: COVID-19 Vaccines; Cross-Sectional Studies; Decision Making; Dental Education; Dental Students; International Association of Dental Students; Mass Vaccination; Multicenter Study; Social Determinants of Health

Online: 30 April 2021 (15:26:07 CEST)

Show abstract| Download PDF| Supplementary Files| Share

Background: Acceleration of mass vaccination strategies is the only pathway to overcome the COVID-19 pandemic. Healthcare professionals and students have a key role in shaping public opinion about vaccines. This study aimed to evaluate the attitudes of dental students globally towards COVID-19 vaccines and explore the potential drivers for students' acceptance levels; Methods: A global cross-sectional study was carried out in February 2021 using an online ques-tionnaire. The study was liaised by the scientific committee of the International Association of Dental Students (IADS), and data was collected through the national and local coordinators of IADS member organizations. The dependent variable was the willingness to take the COVID-19 vaccine, and the independent variables included demographic characteristics, COVID-19-related experi-ence, and the drivers of COVID-19 vaccine-related attitude suggested by the WHO-SAGE; Results: A total of 6639 students from 22 countries representing all world regions responded to the ques-tionnaire properly. Their mean age was 22.06 ± 2.79 (17-40) years, and the majority were females (70.5%), in clinical years (66.8%), and from upper-middle-income economies (45.7%). In general, 22.5% of dental students worldwide were hesitant, and 13.9% rejected COVID-19 vaccines. The students in low- and lower-middle-income (LLMI) economies had significantly higher levels of vaccine hesitancy compared to their peers in upper-middle- and high-income (UMHI) economies (30.4% vs 19.8%; p < 0.001); Conclusions: The global acceptance level of dental students for COVID-19 vaccines was suboptimal, and their worrisome level of vaccine hesitancy was influenced by the socioeconomic context where the dental students live and study. The media and social media, public figures, insufficient knowledge about vaccines, and mistrust of governments and the pharmaceutical industry were barriers to vaccination. The findings of this study call for further implementation of epidemiology (infectious diseases) education within undergraduate dental curricula.

Preprint ARTICLE | doi:10.20944/preprints201803.0272.v1

Bagged Decision Trees Based Scheme of Microgrid Protection Using Windowed Fast Fourier and Wavelet Transforms

Solomon Netsanet Alemu, Jianhua Zhang, Dehua Zheng

Subject: Engineering, Electrical And Electronic Engineering Keywords: Microgrid; Protection; bagged decision tree; Wavelet; FFT

Online: 30 March 2018 (13:57:00 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202007.0191.v1

A Decision Tree Based Intrusion Detection System for Identification of Malicious Web Attacks

Samir Bandyopadhyay, Ratul Chowdhury, Pallabi Banerjee, Soumya Deep Dey, Banani Saha

Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: Intrusion Detection System; NSL-KDD Dataset; One Hot Encoding; Information Gain; Decision Tree

Online: 9 July 2020 (12:23:29 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202104.0001.v1

Assessing Institutional Support to Online Education at Tertiary Level in Bangladesh Coping with Covid-19 Pandemic: An Empirical Study

Mohammad Nur Ullah, Bikram Biswas, Md. Mamun Miah

Subject: Social Sciences, Education Keywords: Assessment; Institutional Support; Online Education; Tertiary Education; Covid-19; Bangladesh

Online: 1 April 2021 (09:03:02 CEST)

Show abstract| Download PDF| Share

Preprint REVIEW | doi:10.20944/preprints201805.0478.v1

Media Health Literacy, eHealth Literacy, and the Role of the Social Environment in Context

Diane Levin-Zamir, Isabella Bertschi

Subject: Social Sciences, Behavior Sciences Keywords: health literacy; media health literacy; ehealth literacy; social environment; health apps; social support; digital health; empowerment

Online: 31 May 2018 (11:45:44 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201807.0288.v1

A Methylcellulose Hydrogel as Support for 3D Plotting of Complex Shaped Calcium Phosphate Scaffolds

Tilman Ahlfeld, Tino Köhler, Charis Czichy, Anja Lode, Michael Gelinsky

Subject: Chemistry And Materials Science, Biomaterials Keywords: calcium phosphate cement; methylcellulose; 3D plotting; support; hydroxyapatite

Online: 16 July 2018 (12:55:42 CEST)

Show abstract| Download PDF| Supplementary Files| Share

Preprint ARTICLE | doi:10.20944/preprints201906.0055.v2

Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO₂

Amin Bemani, Alireza Baghban, Shahaboddin Shamshirband, Amir Mosavi, Peter Csiba, Annamária R. Várkonyi-Kóczy

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: supercritical carbon dioxide; machine learning modeling; acid; artificial intelligence; solubility; artificial neural networks (ANN); adaptive neuro-fuzzy inference system (ANFIS); least-squares support-vector machine (LSSVM); multi-layer perceptron (MLP); engineering applications of artificial intelligence

Online: 31 July 2019 (04:35:26 CEST)

Show abstract| Download PDF| Share

Preprint REVIEW | doi:10.20944/preprints202105.0024.v1

COVID-19 Crisis Management and Cost Estimation Models: Bureaucratic Government Coaction Vs. Spontaneous Social Coordination

Jesús Huerta de Soto, Philipp Bagus, Antonio Sánchez-Bayón

Subject: Business, Economics And Management, Accounting And Taxation Keywords: Decision Making; Cost Estimation; COVID-19 Crisis; Health Economics; Wellbeing Economics; Political Economy

Online: 4 May 2021 (15:27:04 CEST)

Show abstract| Download PDF| Share

Preprint REVIEW | doi:10.20944/preprints201905.0175.v1

Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances

Amir Mosavi, Sina Faizollahzadeh Ardabili, Shahabodin Shamshirband

Subject: Engineering, Electrical And Electronic Engineering Keywords: demand prediction, energy systems; machine learning; artificial neural network (ANN); support vector machines (SVM); neuro-fuzzy; ANFIS; wavelet neural network (WNN); big data; decision tree (DT); ensemble learning; hybrid models; data science; deep learning; renewable energies; energy informatics; prediction; forecasting; energy demand

Online: 14 May 2019 (14:00:40 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201710.0013.v1

Dynamic Scheduling of a Semiconductor Production Line Based on Composite Rule Set

Yumin Ma, Fei Qiao, Fu Zhao, John W. Sutherland

Subject: Engineering, Industrial And Manufacturing Engineering Keywords: Dynamic Scheduling; Semiconductor Manufacturing; Composite Rule Set; Support Vector Regression (SVR)

Online: 3 October 2017 (08:29:45 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202107.0638.v1

Leaf Diseases Detection of Medicinal Plants based on Image Processing and Machine Learning Processes

Payal Bose, SHAWNI DUTTA, Vishal Goyal, Samir K. Bandyopadhyay

Subject: Biology And Life Sciences, Plant Sciences Keywords: Image Processing; Automated Plant Diseases Detection; Histogram Oriented Gradient (HOG); Local Binary Pattern (LBP); Support Vector Machine (SVM)

Online: 28 July 2021 (17:18:04 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201804.0012.v1

MADM Strategy Based on Some Similarity Measures in Interval Bipolar neuTrosophic Set Environment

Surapati Pramanik, Partha Pratim Dey, Florentin Smarandache

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: neutrosophic set; interval bipolar neutrosophic set; multi-attribute decision making; distance measures; similarity measures

Online: 2 April 2018 (08:47:02 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201802.0087.v1

Assessment of Building Damage Risk by Natural Disasters in South Korea Using Decision Tree Analysis

KeumJi Kim, SeongHwan Yoon

Subject: Environmental And Earth Sciences, Environmental Science Keywords: building damage; climate change scenarios; decision tree analysis; limit value; natural disaster; random forest; risk assessment process; South Korea

Online: 12 February 2018 (11:13:37 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201711.0008.v1

Revealing ETC-1922159 Affected Unknown 3^rd Order Combinations of DNA Repair & Genomic Stability Factor RAD51 Family, in Silico

Shriprakash Sinha

Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: WNT pathway; porcupine inhibitor ETC-1922159; sensitivity analysis; colorectal cancer; unknown biological hypotheses; combinatorial search space; support vector ranking; DNA repair and genomic stability factor RAD51

Online: 1 November 2017 (05:08:04 CET)

Show abstract| Download PDF| Share

DNA repair helps in maintaining the proper and healthy functioning for the cells in the human body. Failure in DNA repair process can lead to aberrations as well as tumorous stages. There are various types of damages that a DNA can go through, one of which is the DNA double strand breaks (DSB) that can be repaired via homologous recombination (HR). RAD51 plays a central role in HR and has been implicated as a negative/poor prognostic marker for colorectal adenocarcinoma, with high expression in colorectal cancer. Mechanistically, RAD51AP1 facilitates RAD51 during the repairing process by binding with RAD51 via two DNA binding sites, thus helping in the D-loop formation in the HR process. Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. For example, RAD51AP1-XRCC2 is one such 2^nd order combination whose relation needs to be tested under the influence recently developed Porcupine-WNT inhibitor ETC-1922159. The x-ray repair cross complementing XRCC family is known to work as a mediator or stabilizer for RAD51 during the HR process. The inhibitor is known to suppress Porcupine and thus inhibit a range of oncogenes known to be directly or indirectly affected by the Wnts. In a recent unpublished work in bioRxiv, we had the opportunity to rank these unknown biological hypotheses for down regulated genes at 2^nd order level after the drug was administered. The in silico observations showed that the combination of RAD51AP1-XRCC2 was assigned a relatively lower rank, thus validating the pipeline's efficacy with the confirmed wet lab experiment that indicate that both RAD51AP1 and XRCC2 were down regulated after treatment in cancer cells. Here, we take one step further by in silico analysis of the 3^rd order combinations of RAD51-X-X & RAD51AP1-X-X (X can be known or unknown factor), from a range of 100 randomly picked down regulated genes after ETC-1922159 treatment. The pipeline uses the density based HSIC (Hibert Schmidth Information Criterion) sensitivity index with an rbf (radial basis function) kernel, which is known to be highly effective in sensitivity analysis. Various unknown/unexplored/untested RAD51/RAD51AP1 related 3^rd order biological hypotheses emerge some of which are confirmed in wet lab, while others need to be tested.

Preprint ARTICLE | doi:10.20944/preprints201908.0289.v1

Artificial Intelligence and Convolutional Neural Network for Recognition of Human Interaction by Video from Drone

Ghazal Shamsipour, Saied Pirasteh

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: drone video; human action recognition; CNN; Support vector machine (SVM)

Online: 28 August 2019 (03:52:22 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202103.0298.v1

Sintered Glass Monoliths as New Supports for Affinity Columns

Marco Wilke, Bettina Röder, Martin Paul, Michael G. Weller

Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Affinity chromatography; matrix; solid support; resin; support materials; glass filter; glass frit; high-pressure; HPLC; FPLC; antibodies; immunoglobulins; purification; downstream processing; protein purification; preparative; analytical; separation; clean-up; automation

Online: 11 March 2021 (08:37:01 CET)

Show abstract| Download PDF| Supplementary Files| Share

A novel stationary phase for affinity separations is presented. This material is based on sintered borosilicate glass readily available as semi-finished filter plates with defined porosity and surface area. The material shows fast binding kinetics and excellent long-term stability under real application conditions due to lacking macropores and high mechanical rigidity. The glass surface can be easily modified with standard organosilane chemistry to immobilize selective binders or other molecules used for biointeraction. In this paper, the manufacturing of the columns and their respective column holders by 3D printing is shown in detail. The model system protein A/IgG was chosen as an example to examine the properties of such monolithic columns under realistic application conditions. Several specifications, such as (dynamic) IgG capacity, pressure stability, long-term performance, productivity, non-specific binding, and peak shape, are presented. It could be shown that due to the very high separation speed, 250 mg antibody per hour and column can be collected, which surpasses the productivity of most standard columns of the same size. The total IgG capacity of the shown columns is around 4 mg (5.5 mg/mL), which is sufficient for most tasks in research laboratories. The cycle time of an IgG separation can be less than 1 minute. Due to the glass material's excellent pressure resistance, these columns are compatible with standard HPLC systems. This is usually not the case with standard affinity columns, limited to manual use or application in low-pressure systems. The use of a standard HPLC system also improves the ability for automation, which enables the purification of hundreds of cell supernatants in one day. The sharp peak shape of the elution leads to an enrichment effect, which might increase the concentration of IgG by a factor of 3. The final concentration of IgG can be around 7.5 mg/mL without the need for an additional nanofiltration step. The purity of the IgG was > 95% in one step and nearly 99% with a second polishing run.

Preprint ARTICLE | doi:10.20944/preprints201711.0160.v1

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

Chunyan Yu, Xiaoxu Li, Hong Yang, Yinghong Li, Weiwei Xue, Yuzong Chen, Lin Tao, Feng Zhu

Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: false discovery rate; machine learning; protein function prediction; support vector machine; BLAST

Online: 24 November 2017 (11:14:05 CET)

Show abstract| Download PDF| Supplementary Files| Share

The knowledge of protein function is essential for the study of biological processes, the understanding of disease mechanism and the exploration of novel therapeutic target. Apart from experimental methods, a number of in-silico approaches have been developed and extensively used for protein function prediction. Among these approaches, BLAST predicts functions based on protein sequence similarity, and machine learning predicts functional families from protein sequences irrespective of their similarity, which complements BLAST and other methods in predicting diverse classes of proteins including distantly related proteins and homologous proteins of different functions. However, their identification accuracies and the false discovery rate have not yet been assessed so far, which greatly limits the usage of these prediction algorithms. Herein, a comprehensive comparison of the performances among four popular functional prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these algorithms were systematically assessed by four metrics (sensitivity, specificity, accuracy and Matthews correlation coefficient) based on the independent test datasets generated from 93 protein families defined by UniProtKB Keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model species (homo sapiens, arabidopsis thaliana, saccharomyces cerevisiae and mycobacterium tuberculosis). As a result, the substantially higher sensitivity and stability of BLAST and SVM were observed compared with that of PNN and KNN. But the machine learning algorithms (PNN, KNN and SVM) were found capable of significantly reducing the false discovery rate (SVM < PNN ≈ KNN). In summary, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research.

Preprint ARTICLE | doi:10.20944/preprints201709.0114.v1

Seawater Temperature Prediction Method for Sustainable Marine Aquaculture

Takanobu Otsuka, Yuji Kitazawa, Takayuki Ito

Subject: Computer Science And Mathematics, Information Systems Keywords: WSN; IoT; seawater temperature prediction; marine aquaculture support

Online: 23 September 2017 (11:31:13 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201711.0124.v1

NN-Harmonic Mean Aggregation Operators Based MCGDM Strategy in Neutrosophic Number Environment

Kalyan Mondal, Surapati Pramanik, Bibhas C. Giri, Florentin Smarandache

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: neutrosophic number; neutrosophic number harmonic mean operator (NNHMO); neutrosophic number weighted harmonic mean operator (NNWHMO); cosine function, score function; multi criteria group decision making

Online: 20 November 2017 (09:53:31 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201806.0377.v1

Crisis, Risk Culture and the Role Model of the Honourable Merchant

Jürgen Bott, Udo Milkau

Subject: Business, Economics And Management, Finance Keywords: risk culture; honourable merchant; decision-making; responsibility

Online: 25 June 2018 (10:21:18 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201912.0249.v1

A Practical Guide for Conducting Calibration and Decision-Making Optimisation with Complex Ecological Models

Stephanie Mahévas, Victor Picheny, Patrick Lambert, Nicolas Dumoulin, Lauriane Rouan, Jean-Christophe Soulié, Dimo Brockhoff, Sigrid Lehuta, Rodolphe Le Riche, Robert Faivre, Hilaire Drouineau

Subject: Biology And Life Sciences, Ecology, Evolution, Behavior And Systematics Keywords: optimisation; complex models; calibration; identification; parameter estimation; decision-making; pre-processing; post-processing; tracability

Online: 19 December 2019 (07:15:49 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202005.0331.v3

Multi-Objective Optimization of Periodical Objectives within a Process

Moses Oyaro Okello

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: optimization; multi-objective optimization; decision making; Time

Online: 3 February 2022 (17:30:32 CET)

Show abstract| Download PDF| Share

Multi-objective optimization (MOO) is an optimization involving minimization or maximization of several objective functions more than the conventional one objective optimization, which is useful in many fields. Many of the current methodologies addresses challenges and solutions that attempt to solve simultaneously several Objectives with multiple constraints subjoined to each. Often MOO are generally subjected to linear inequality, equality and or bounded constraint that prevent all objectives from being optimized at once. This paper reviews some recent articles in area of MOO and presents deep analysis of Random and Uniform Entry-Exit time of objectives. It further break down process into sub-process and then provide some new concepts for solving problems in MOO, which comes due to periodical objectives that do not stay for the entire duration of process lifetime, unlike permanent objectives which are optimized once for the entire process duration. A methodology based on partial optimization that optimizes each objective iteratively and weight convergence method that optimizes sub-group of objectives are given. Furthermore, another method is introduced which involve objective classification, ranking, estimation and prediction where objectives are classified based on their properties, and ranked using a given criteria and in addition estimated for an optimal weight point (pareto optimal point) if it certifies a coveted optimal weight point. Then finally predicted to find how far it deviates from the estimated optimal weight point. A Sample Mathematical Tri-Objectives and Real world Optimization was analyzed using partial method, ranking and classification method, the result showed that an objective can be added or removed without affecting previous or existing optimal solutions. Therefore suitable for handling time governed MOO. Although this paper presents concepts work only, it’s practical application are beyond the scope of this paper, however base on analysis and examples presented, the concept is worthy of igniting further research and application.

Preprint ARTICLE | doi:10.20944/preprints202011.0519.v1

AmazonForest: In-silico Meta-Prediction of Pathogenic Variants

Helber Palheta, Wanderson Gonçalves Gonçalves, Leonardo Miranda Brito, Arthur Ribeiro dos Santos, Marlon Matsumoto, Ândrea Kely Ribeiro-dos-Santos, Gilderlanio Santana Araújo

Subject: Biology And Life Sciences, Anatomy And Physiology Keywords: Meta-prediction; Encoding data; ClinVar; Classification; Random Forest; Naive Bayes; Support Vector Machine

Online: 19 November 2020 (16:43:51 CET)

Show abstract| Download PDF| Supplementary Files| Share

Search Results

710 articles found