scholarly journals Desalination plants as plankton sampling devices in temporal studies: proof-of-concept and suggestions for the future

2009 ◽  
Vol 7 (5) ◽  
pp. 363-370 ◽  
Author(s):  
Mary A. Sewell ◽  
Jennifer A. Jury
Keyword(s):  
Proof Of Concept ◽  
The Future ◽  
Desalination Plants

scholarly journals An economic analysis of ransomware and its welfare consequences

2020 ◽  
Vol 7 (3) ◽  
pp. 190023
Author(s):  
J. Hernandez-Castro ◽  
A. Cartwright ◽  
E. Cartwright
Keyword(s):  
Economic Analysis ◽  
Price Discrimination ◽  
Proof Of Concept ◽  
Welfare Costs ◽  
Pilot Survey ◽  
Uniform Pricing ◽  
The Future ◽  
Welfare Consequences ◽  
New Form

We present in this work an economic analysis of ransomware, a relatively new form of cyber-enabled extortion. We look at how the illegal gains of the criminals will depend on the strategies they use, examining uniform pricing and price discrimination. We also explore the welfare costs to society of such strategies. In addition, we present the results of a pilot survey which demonstrate proof of concept in evaluating the costs of ransomware attacks. We discuss at each stage whether the different strategies we analyse have been encountered already in existing malware, and the likelihood of them being implemented in the future. We hope this work will provide some useful insights for predicting how ransomware may evolve in the future.

Towards Implementing a Collaborative Manufacturing Cloud Platform: Experimenting Testbeds Aiming Asset Efficiency

2020 ◽  
Author(s):  
Nuno Santos ◽  
Paula Monteiro ◽  
Francisco Morais ◽  
Jaime Pereira ◽  
Daniel Dias ◽  
...  
Keyword(s):  
Supply Chain ◽  
Internet Of Things ◽  
Proof Of Concept ◽  
Cloud Platform ◽  
Industrial Internet Of Things ◽  
Manufacturing Environment ◽  
Collaborative Manufacturing ◽  
Manufacturing Operations ◽  
Industrial Internet ◽  
The Future

Abstract Developing Industrial Internet of Things (IIoT) systems requires addressing challenges that range from acquiring data at the level of the shopfloor, integrated at the edge level and managing it at the cloud level. Managing manufacturing operations at the cloud level arose the opportunity for extending decisions to entities of the supply chain in a collaborative way. Not only it has arisen many challenges due to several interoperability needs; but also in properly defining an effective way to take advantage of the available data, leading to Industrial Digital Thread (IDT) and Asset Efficiency (AE) implementing. This paper discusses implementation concerns for a collaborative manufacturing environment in an IIoT system in order to monitor equipment’s AE. Each concern was addressed in a separate proof of concept testbed. The demonstration is based in a project for the IIoT domain called PRODUTECH-SIF (Solutions for the Industry of the Future).

When Scents Help Me Remember My Password

2021 ◽  
Vol 18 (3) ◽  
pp. 1-18
Author(s):  
Anas Ali Alkasasbeh ◽  
Fotios Spyridonis ◽  
Gheorghita Ghinea
Keyword(s):  
Quality Of Experience ◽  
T Test ◽  
Proof Of Concept ◽  
Control Groups ◽  
Information Recall ◽  
Significant Difference ◽  
The Future ◽  
Experimental Group

Current authentication processes overwhelmingly rely on audiovisual data, comprising images, text or audio. However, the use of olfactory data (scents) has remained unexploited in the authentication process, notwithstanding their verified potential to act as cues for information recall. Accordingly, in this paper, a new authentication process is proposed in which olfactory media are used as cues in the login phase. To this end, PassSmell , a proof of concept authentication application, is developed in which words and olfactory media act as passwords and olfactory passwords, respectively. In order to evaluate the potential of PassSmell, two different versions were developed, namely one which was olfactory-enhanced and another which did not employ olfactory media. Forty-two participants were invited to take part in the experiment, evenly split into a control and experimental group. For assessment purposes, we recorded the time taken to logon as well as the number of failed/successful login attempts; we also asked users to complete a Quality of Experience (QoE) questionnaire. In terms of time taken, a significant difference was found between the experimental and the control groups, as determined by an independent sample t-test. Similar results were found with respect to average scores and the number of successful attempts. Regarding user QoE, having olfactory media with words influenced the users positively, emphasizing the potential of using this kind of authentication application in the future.

scholarly journals Effects of Polylactide Copolymer Implants and Platelet-Rich Plasma on Bone Regeneration within a Large Calvarial Defect in Sheep

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Bartłomiej Błaszczyk ◽  
Wojciech Kaspera ◽  
Krzysztof Ficek ◽  
Maciej Kajor ◽  
Marcin Binkowski ◽  
...  
Keyword(s):  
Bone Formation ◽  
Bone Regeneration ◽  
Bone Healing ◽  
Platelet Rich Plasma ◽  
Calvarial Defect ◽  
Proof Of Concept ◽  
Adult Sheep ◽  
The Future ◽  
Calvarial Defects

The aim of this study was to verify whether L-lactide/DL-lactide copolymer 80/20 (PLDLLA) and platelet-rich plasma (PRP) trigger bone formation within critical-sized calvarial defects in adult sheep (n=6). Two craniectomies, each ca. 3 cm in diameter, were created in each animal. The first craniectomy was protected with an inner polylactide membrane, filled with PRP-polylactide granules, and covered with outer polylactide membrane. The second control craniectomy was left untreated. The animals were euthanized at 6, 7, 17, 19, 33, and 34 weeks after surgery, and the quality and the rate of reossification were assessed histomorphometrically and microtomographically. The study demonstrated that application of implants made of PLDLLA 80/20 combined with an osteopromotive substance (e.g., PRP) may promote bone healing in large calvarial defect in sheep. These promising proof-of-concept studies need to be verified in the future on a larger cohort of animals and over a longer period of time in order to draw definitive conclusions.

scholarly journals Towards a Generalised Metaheuristic Model for Continuous Optimisation Problems

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2046
Author(s):  
Jorge M. Cruz-Duarte ◽  
José C. Ortiz-Bayliss ◽  
Iván Amaya ◽  
Yong Shi ◽  
Hugo Terashima-Marín ◽  
...  
Keyword(s):  
Building Blocks ◽  
Proof Of Concept ◽  
Proposed Model ◽  
The Future ◽  
Simple Heuristics

Metaheuristics have become a widely used approach for solving a variety of practical problems. The literature is full of diverse metaheuristics based on outstanding ideas and with proven excellent capabilities. Nonetheless, oftentimes metaheuristics claim novelty when they are just recombining elements from other methods. Hence, the need for a standard metaheuristic model is vital to stop the current frenetic tendency of proposing methods chiefly based on their inspirational source. This work introduces a first step to a generalised and mathematically formal metaheuristic model, which can be used for studying and improving them. This model is based on a scheme of simple heuristics, which perform as building blocks that can be modified depending on the application. For this purpose, we define and detail all components and concepts of a metaheuristic (i.e., its search operators), such as heuristics. Furthermore, we also provide some ideas to take into account for exploring other search operator configurations in the future. To illustrate the proposed model, we analyse search operators from four well-known metaheuristics employed in continuous optimisation problems as a proof-of-concept. From them, we derive 20 different approaches and use them for solving some benchmark functions with different landscapes. Data show the remarkable capability of our methodology for building metaheuristics and detecting which operator to choose depending on the problem to solve. Moreover, we outline and discuss several future extensions of this model to various problem and solver domains.

scholarly journals An Automated Scoring of Clinical Asthma Score: Proof of Concept and the Future Possibility

2021 ◽  
Vol 3 (1) ◽  
pp. e0319
Author(s):  
Sally Al-Omar ◽  
Alex Lepage-Farrell ◽  
Atsushi Kawaguchi ◽  
Guillaume Emeriaud
Keyword(s):  
Proof Of Concept ◽  
Automated Scoring ◽  
Future Possibility ◽  
The Future ◽  
Clinical Asthma

Nuancing Perspective

2021 ◽  
Vol 52 (4) ◽  
pp. 238-249
Author(s):  
Jacob Israelashvili ◽  
Anat Perry
Keyword(s):  
Feedback Condition ◽  
Proof Of Concept ◽  
Emotional Experiences ◽  
Feedback Group ◽  
The Future

Abstract. Two experiments manipulated participants’ familiarity with another person and examined their performance in future understanding of that person’s emotions. To gain familiarity, participants watched several videos of the target sharing experiences and rated her emotions. In the Feedback condition, perceivers learned about the actual emotions the target felt. In the Control condition, perceivers completed identical recognition tasks but did not know the target’s own emotion ratings. Studies ( Ntotal = 398; one preregistered) found that the Feedback group was more accurate than the Control in future understanding of the target’s emotions. Results provide a proof-of-concept demonstration that brief preliminary learning about past emotional experiences of another person can give one a more accurate understanding of the person in the future.

scholarly journals Predicting the future of prediction

2020 ◽  
pp. 80-90
Author(s):  
Liliana Lindberg
Keyword(s):  
Language Processing ◽  
Current Speed ◽  
Northern Europe ◽  
Paradigm Change ◽  
Proof Of Concept ◽  
Disruptive Technologies ◽  
The World ◽  
The Future ◽  
High Level ◽  
Machine Learning Models

Artificial Intelligence (AI) is, without a doubt, one of the disruptive technologies that will make an impact on the way we live, work and interact with each other now and in the future. This article highlights the current speed of AI development starting with some background on Computer Vision and then describes the latest research in the field of Natural Language Processing (NLP). Many of the traditional machine learning models have gained strength with the advent of Deep Learning and companies are embarking on their AI journeys; some are succeeding and some others are facing challenges when moving from proof-of-concept to production. An overview of the most common challenges for companies in Northern Europe is presented to bring a perspective of what is happening in other parts of the world. In the end, two examples of how face recognition is being applied in similar use cases both in China and Sweden are also described. The cases are used to compare the effects of those implementations as a framework of the importance of regulation in the future. All the topics are discussed at a high level to give a quick outline of how we can position ourselves today to best prepare for a paradigm change that has already started.

scholarly journals Waltzing around Sacred Cows on the Way to the Future

2017 ◽  
Vol 56 (3) ◽  
Author(s):  
Stephen M. Brecher
Keyword(s):  
Blood Culture ◽  
Positive Blood Culture ◽  
Clinical Microbiology ◽  
Clinical Microbiology Laboratory ◽  
Proof Of Concept ◽  
Microbiology Laboratory ◽  
Trained Personnel ◽  
The Future ◽  
Medical Technologists ◽  
The Way

ABSTRACT Our mostly manual, agar-based clinical microbiology laboratory is slowly but steadily being redefined by automation and innovation. Ironically, the oldest test, the Gram stain test, is still manually read and interpreted by trained personnel. In a proof-of-concept study, Smith et al. (J. Clin. Microbiol. 56:e01521-17, 2018, https://doi.org/10.1128/JCM.01521-17 ) used computer imaging with a deep convolutional neural network to examine and interpret Gram-stained slides from positive blood culture bottles. In light of the shortage of medical technologists/microbiologists and the need for results from positive blood culture bottles 24/7, this paper paves the way for the next innovations for the clinical microbiology laboratory of the future.

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