scholarly journals Coordinating Measurements in Uncertain Participatory Sensing Settings

2018 ◽  
Vol 61 ◽  
pp. 433-474 ◽  
Author(s):  
Alexandros Zenonos ◽  
Sebastian Stein ◽  
Nicholas R. Jennings
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Low Cost ◽  
Cost Effective ◽  
Environmental Data ◽  
Participatory Sensing ◽  
Sufficient Information ◽  
Mobility Patterns ◽  
Computationally Efficient ◽  
The Impact

Environmental monitoring allows authorities to understand the impact of potentially harmful phenomena, such as air pollution, excessive noise, and radiation. Recently, there has been considerable interest in participatory sensing as a paradigm for such large-scale data collection because it is cost-effective and able to capture more fine-grained data than traditional approaches that use stationary sensors scattered in cities. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-interested actors that have their own goals and make local decisions about when and where to take measurements. This can lead to highly inefficient outcomes, where observations are either taken redundantly or do not provide sufficient information about key areas of interest. To address these challenges, it is necessary to guide and to coordinate participants, so they take measurements when it is most informative. To this end, we develop a computationally-efficient coordination algorithm (adaptive Best-Match) that suggests to users when and where to take measurements. Our algorithm exploits probabilistic knowledge of human mobility patterns, but explicitly considers the uncertainty of these patterns and the potential unwillingness of people to take measurements when requested to do so. In particular, our algorithm uses a local search technique, clustering and random simulations to map participants to measurements that need to be taken in space and time. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the current state of the art by up to 24% in terms of utility gained.

Motivating Compliance: Firm Response to Mandatory Disclosure Policies

2018 ◽  
Vol 32 (2) ◽  
pp. 103-119
Author(s):  
Colleen M. Boland ◽  
Chris E. Hogan ◽  
Marilyn F. Johnson
Keyword(s):  
Conflict Of Interest ◽  
Low Cost ◽  
Cost Effective ◽  
Data Availability ◽  
Policy Adoption ◽  
Future Research ◽  
Regulated Firms ◽  
Firm Response ◽  
Disclosure Rules ◽  
The Impact

SYNOPSIS Mandatory existence disclosure rules require an organization to disclose a policy's existence, but not its content. We examine policy adoption frequencies in the year immediately after the IRS required mandatory existence disclosure by nonprofits of various governance policies. We also examine adoption frequencies in the year of the subsequent change from mandatory existence disclosure to a disclose-and-explain regime that required supplemental disclosures about the content and implementation of conflict of interest policies. Our results suggest that in areas where there is unclear regulatory authority, mandatory existence disclosure is an effective and low cost regulatory device for encouraging the adoption of policies desired by regulators, provided those policies are cost-effective for regulated firms to implement. In addition, we find that disclose-and-explain regulatory regimes provide stronger incentives for policy adoption than do mandatory existence disclosure regimes and also discourage “check the box” behavior. Future research should examine the impact of mandatory existence disclosure rules in the year that the regulation is implemented. Data Availability: Data are available from sources cited in the text.

scholarly journals Anti-meningococcal B vaccination in Italian adolescents: a cost-effective health opportunity

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
D Panatto ◽  
P Landa ◽  
D Amicizia ◽  
P L Lai ◽  
E Lecini ◽  
...  
Keyword(s):  
Large Scale ◽  
Public Health Problem ◽  
Vaccination Strategy ◽  
Cost Effective ◽  
Dose Schedule ◽  
Developed Countries ◽  
Invasive Disease ◽  
International Setting ◽  
The Impact

Abstract Background Invasive disease due to Neisseria meningitidis (Nm) is a serious public health problem even in developed countries, owing to its high lethality rate (8-15%) and the invalidating sequelae suffered by many (up to 60%) survivors. As the microorganism is transmitted via the airborne route, the only available weapon in the fight against Nm invasive disease is vaccination. Our aim was to carry out an HTA to evaluate the costs and benefits of anti-meningococcal B (MenB) vaccination with Trumenba® in adolescents in Italy, while also considering the impact of this new vaccination strategy on organizational and ethics aspects. Methods A lifetime Markov model was developed. MenB vaccination with the two-dose schedule of Trumenba® in adolescents was compared with 'non-vaccination'. Two perspectives were considered: the National Health Service (NHS) and society. Three disease phases were defined: acute, post-acute and long-term. Epidemiological, economic and health utilities data were taken from Italian and international literature. The analysis was conducted by means of Microsoft Excel 2010®. Results Our study indicated that vaccinating adolescents (11th year of life) with Trumenba® was cost-effective with an ICER = € 7,912/QALY from the NHS perspective and € 7,758/QALY from the perspective of society. Vaccinating adolescents reduces the number of cases of disease due to meningococcus B in one of the periods of highest incidence of the disease, resulting in significant economic and health savings. Conclusions This is the first study to evaluate the overall impact of free MenB vaccination in adolescents both in Italy and in the international setting. Although cases of invasive disease due to meningococcus B are few, if the overall impact of the disease is adequately considered, it becomes clear that including anti-meningococcal B vaccination into the immunization program for adolescents is strongly recommended from the health and economic standpoints. Key messages Free, large-scale MenB vaccination is key to strengthening the global fight against invasive meningococcal disease. Anti-meningococcal B vaccination in adolescents is a cost-effective health opportunity.

The Impact of Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility

2021 ◽  
Vol 7 (4) ◽  
pp. 1-24
Author(s):  
Douglas Do Couto Teixeira ◽  
Aline Carneiro Viana ◽  
Jussara M. Almeida ◽  
Mrio S. Alvim
Keyword(s):  
State Of The Art ◽  
Human Mobility ◽  
Contextual Information ◽  
Prediction Method ◽  
Mobility Prediction ◽  
Mobility Patterns ◽  
Distinct Cell ◽  
Inherent Nature ◽  
The One ◽  
The Impact

Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one’s routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, such as variations in individual preferences, weather, traffic, or even a person’s social contacts, can affect mobility patterns and make its modeling significantly more challenging. A fundamental approach to study mobility-related behavior is to assess how predictable such behavior is, deriving theoretical limits on the accuracy that a prediction model can achieve given a specific dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in that dataset, filtering out factors that depend on the specificities of the prediction method adopted. However, the current state-of-the-art method to estimate predictability in human mobility suffers from two major limitations: low interpretability and hardness to incorporate external factors that are known to help mobility prediction (i.e., contextual information). In this article, we revisit this state-of-the-art method, aiming at tackling these limitations. Specifically, we conduct a thorough analysis of how this widely used method works by looking into two different metrics that are easier to understand and, at the same time, capture reasonably well the effects of the original technique. We evaluate these metrics in the context of two different mobility prediction tasks, notably, next cell and next distinct cell prediction, which have different degrees of difficulty. Additionally, we propose alternative strategies to incorporate different types of contextual information into the existing technique. Our evaluation of these strategies offer quantitative measures of the impact of adding context to the predictability estimate, revealing the challenges associated with doing so in practical scenarios.

Development of All-Plastic Microvalve Array for Multiplexed Immunoassay

2014 ◽  
Author(s):  
Shancy Augustine ◽  
Pan Gu ◽  
Xiangjun Zheng ◽  
Toshikazu Nishida ◽  
Z. Hugh Fan
Keyword(s):  
Large Scale ◽  
Low Cost ◽  
Simultaneous Detection ◽  
Cost Effective ◽  
Negative Control ◽  
Sensitive Material ◽  
Detection Scheme ◽  
Power Battery ◽  
Multiple Analytes ◽  
Multiplexed Immunoassay

There is a need for low-cost immunoassays that measure the presence and concentration of multiple harmful agents in one device. Currently, comparable immunoassays employ a one-analyte-per-test format that is time consuming and not cost effective for the requirement of detecting multiple analytes in a single sample. For instance, if a spectrum of harmful agents, including E. coli O157, cholera toxin, and Salmonella typhimurium, should be simultaneously monitored in foods and drinking water, then a one-analyte-per-test would be inefficient. This work demonstrates a platform capable of simultaneous detection of multiple analytes in a single, low-cost, microvalve array-enabled multiplexed immunoassay. This multiplexed immunoassay platform is demonstrated in a prototype COC (cyclic olefin copolymer) device with a 2×3 array in which 6 analytes can be detected simultaneously. In order to contain and regulate the flow of reagents in the multichannel device, an array of microfluidic valves actuated by a thermally expandable material and microfabricated resistors have been developed to direct the flow to the necessary assay sites. The microvalve-based immunoassay is shown to be reliable, easy to operate, and compatible with large-scale integration. The all-plastic microvalves use paraffin wax as the thermally sensitive material which drastically reduces power consumption by latching upon closing so that pulsed power is required only to close and latch the microvalve until it is necessary to re-open the valve. The multiplexed detection scheme has been demonstrated by using three proteins, C reactive protein (CRP) and transferrin, both of which are biomarkers associated with traumatic brain injury (TBI) as well as bovine serum albumin (BSA) as the negative control. Since there are no external bulky pneumatic accessories required to operate/latch the microvalves in the device, this compact, thermally actuated and latching microvalve-enabled multiplexed immunoassay has the potential to realize a portable, low power, battery operated microfluidic device for biological assays.

scholarly journals Modelling key market impacts and land allocation for biofuel production and forestry

2006 ◽  
Vol 2006 ◽  
pp. 1-12
Author(s):  
A. Korobeinikov ◽  
P. Read ◽  
A. Parshotam ◽  
J. Lermit
Keyword(s):  
Carbon Dioxide ◽  
Atmospheric Carbon Dioxide ◽  
Large Scale ◽  
Low Cost ◽  
Wood Products ◽  
Biofuel Production ◽  
Fuel Wood ◽  
Carbon Dioxide Levels ◽  
The Cost ◽  
The Impact

It has been suggested that the large scale use of biofuel, that is, fuel derived from biological materials, especially in combination with reforestation of large areas, can lead to a low-cost reduction of atmospheric carbon dioxide levels. In this paper, a model of three markets: fuel, wood products, and land are considered with the aim of evaluating the impact of large scale biofuel production and forestry on these markets, and to estimate the cost of a policy aimed at the reduction of carbon dioxide in the atmosphere. It is shown that the costs are lower than had been previously expected.

scholarly journals Trajectory-User Linking via Variational AutoEncoder

2018 ◽  
Author(s):  
Fan Zhou ◽  
Qiang Gao ◽  
Goce Trajcevski ◽  
Kunpeng Zhang ◽  
Ting Zhong ◽  
...  
Keyword(s):  
Latent Variables ◽  
Large Scale ◽  
Human Mobility ◽  
Hierarchical Structures ◽  
High Dimensional ◽  
Generation Process ◽  
Mobility Patterns ◽  
Variational Autoencoder ◽  
Hidden States ◽  
Structural Semantics

Trajectory-User Linking (TUL) is an essential task in Geo-tagged social media (GTSM) applications, enabling personalized Point of Interest (POI) recommendation and activity identification. Existing works on mining mobility patterns often model trajectories using Markov Chains (MC) or recurrent neural networks (RNN) -- either assuming independence between non-adjacent locations or following a shallow generation process. However, most of them ignore the fact that human trajectories are often sparse, high-dimensional and may contain embedded hierarchical structures. We tackle the TUL problem with a semi-supervised learning framework, called TULVAE (TUL via Variational AutoEncoder), which learns the human mobility in a neural generative architecture with stochastic latent variables that span hidden states in RNN. TULVAE alleviates the data sparsity problem by leveraging large-scale unlabeled data and represents the hierarchical and structural semantics of trajectories with high-dimensional latent variables. Our experiments demonstrate that TULVAE improves efficiency and linking performance in real GTSM datasets, in comparison to existing methods.

Performance of rapid diagnostic tests for HCV infection in serum or plasma

2021 ◽  
Author(s):  
Stéphane Chevaliez ◽  
Françoise Roudot-Thoraval ◽  
Christophe Hézode ◽  
Jean-Michel Pawlotsky ◽  
Richard Njouom
Keyword(s):  
Hepatitis C ◽  
Diagnostic Tests ◽  
Large Scale ◽  
Low Cost ◽  
Antibody Test ◽  
Cost Effective ◽  
Rapid Diagnostic Tests ◽  
Treatment Programs ◽  
Clinical Sensitivity ◽  
Cost Treatment

Aim: HCV diagnosis will become the bottleneck in eliminating hepatitis C. Simple, accurate and cost-effective testing strategies are urgently needed to improve hepatitis C screening and diagnosis. Materials & methods: Performance of seven rapid diagnostic tests (RDT) have been assessed in a large series (n = 498) of serum or plasma specimens collected in France and in Cameroon. Results: Specificity varied from 96.1 to 100%. The clinical sensitivity, compared with immunoassays as the reference, was high for all seven RDT (97.2–100%). The Multisure HCV antibody assay and OraQuick HCV rapid antibody test reached sensitivity ≥99%. Conclusion: A number of RDT may be suitable for WHO prequalification and may be implemented in the framework of large-scale low-cost treatment programs to achieve the WHO viral hepatitis objectives by 2030.

scholarly journals Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings

2019 ◽  
Vol 9 (14) ◽  
pp. 2861 ◽  
Author(s):  
Alessandro Crivellari ◽  
Euro Beinat
Keyword(s):  
Neural Network ◽  
Motion Tracking ◽  
Large Scale ◽  
Short Term Memory ◽  
Human Mobility ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Short Term

The interest in human mobility analysis has increased with the rapid growth of positioning technology and motion tracking, leading to a variety of studies based on trajectory recordings. Mapping the routes that people commonly perform was revealed to be very useful for location-based service applications, where individual mobility behaviors can potentially disclose meaningful information about each customer and be fruitfully used for personalized recommendation systems. This paper tackles a novel trajectory labeling problem related to the context of user profiling in “smart” tourism, inferring the nationality of individual users on the basis of their motion trajectories. In particular, we use large-scale motion traces of short-term foreign visitors as a way of detecting the nationality of individuals. This task is not trivial, relying on the hypothesis that foreign tourists of different nationalities may not only visit different locations, but also move in a different way between the same locations. The problem is defined as a multinomial classification with a few tens of classes (nationalities) and sparse location-based trajectory data. We hereby propose a machine learning-based methodology, consisting of a long short-term memory (LSTM) neural network trained on vector representations of locations, in order to capture the underlying semantics of user mobility patterns. Experiments conducted on a real-world big dataset demonstrate that our method achieves considerably higher performances than baseline and traditional approaches.

scholarly journals The impact of secondary ice production on Arctic stratocumulus

2020 ◽  
Vol 20 (3) ◽  
pp. 1301-1316
Author(s):  
Georgia Sotiropoulou ◽  
Sylvia Sullivan ◽  
Julien Savre ◽  
Gary Lloyd ◽  
Thomas Lachlan-Cope ◽  
...  
Keyword(s):  
Large Scale ◽  
Cloud Condensation Nuclei ◽  
The Arctic ◽  
Computationally Efficient ◽  
Eddy Simulation ◽  
Scale Models ◽  
Break Up ◽  
Ice Production ◽  
Low Sensitivity ◽  
The Impact

Abstract. In situ measurements of Arctic clouds frequently show that ice crystal number concentrations (ICNCs) are much higher than the number of available ice-nucleating particles (INPs), suggesting that secondary ice production (SIP) may be active. Here we use a Lagrangian parcel model (LPM) and a large-eddy simulation (LES) to investigate the impact of three SIP mechanisms (rime splintering, break-up from ice–ice collisions and drop shattering) on a summer Arctic stratocumulus case observed during the Aerosol-Cloud Coupling And Climate Interactions in the Arctic (ACCACIA) campaign. Primary ice alone cannot explain the observed ICNCs, and drop shattering is ineffective in the examined conditions. Only the combination of both rime splintering (RS) and collisional break-up (BR) can explain the observed ICNCs, since both of these mechanisms are weak when activated alone. In contrast to RS, BR is currently not represented in large-scale models; however our results indicate that this may also be a critical ice-multiplication mechanism. In general, low sensitivity of the ICNCs to the assumed INP, to the cloud condensation nuclei (CCN) conditions and also to the choice of BR parameterization is found. Finally, we show that a simplified treatment of SIP, using a LPM constrained by a LES and/or observations, provides a realistic yet computationally efficient way to study SIP effects on clouds. This method can eventually serve as a way to parameterize SIP processes in large-scale models.

scholarly journals Effect of Protocatechuic Acid on Euglena gracilis Growth and Accumulation of Metabolites

2020 ◽  
Vol 12 (21) ◽  
pp. 9158
Author(s):  
Xiaomiao Tan ◽  
Jiangyu Zhu ◽  
Minato Wakisaka
Keyword(s):  
Euglena Gracilis ◽  
Large Scale ◽  
Protocatechuic Acid ◽  
Biomass Yield ◽  
Growth Promotion ◽  
Low Cost ◽  
Cost Effective ◽  
Control Group ◽  
Biological Functions ◽  
First Time

The development of efficient, environmentally friendly, low-cost approaches used to boost the growth of microalgae is urgently required to meet the increasing demands for food supplements, cosmetics, and biofuels. In this study, the growth promotion effects of protocatechuic acid (PCA) in the freshwater microalga Euglena gracilis were confirmed for the first time. PCA is a simple phenolic compound derived from natural plants and has a range of biological functions. The highest biomass yield, 3.1-fold higher than that of the control, used at 1.3 g·L−1, was obtained at 800 mg·L−1 of PCA. The yields of the metabolites chlorophyll a, carotenoids, and paramylon in the presence of PCA at 800 mg·L−1 were 3.1, 3.3, and 1.7 times higher than those of the control group, respectively. The highest paramylon yield was achieved at a lower dosage of PCA (100 mg·L−1), which is considered to be feasible for economic paramylon production. The growth and biosynthesis of metabolites stimulated by phytochemicals such as PCA could be an efficient and cost-effective strategy to enhance the productivity of microalgae in large-scale cultivations.

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