scholarly journals Predicting the Upcoming Services of Vacant Taxis near Fixed Locations Using Taxi Trajectories

2019 ◽  
Vol 8 (7) ◽  
pp. 295 ◽  
Author(s):  
Chunchun Hu ◽  
Jean-Claude Thill
Keyword(s):  
Large Scale ◽  
Viterbi Algorithm ◽  
Predictive Accuracy ◽  
Confusion Matrix ◽  
Time Of Day ◽  
Modeling Framework ◽  
Factors Affecting ◽  
Large Cities ◽  
Fuzzy Partitioning ◽  
Taxi Industry

Emerging on-line reservation services and special car services have greatly affected the development of the taxi industry. Surprisingly, taking a taxi is still a significant problem in many large cities. In this paper, we present an effective solution based on the Hidden Markov Model to predict the upcoming services of vacant taxis that appear at some fixed locations and at specific times. The model introduces a weighted confusion matrix and a modified Viterbi algorithm, combining the factors of time of day and traffic conditions. In our framework, the hotspot or hidden states extraction is implemented through kernel density estimation (KDE) and fuzzy partitioning of traffic zones is done via a Fuzzy C Means (FCM) algorithm. We implement the proposed model on a large-scale dataset of taxi trajectories in Beijing. In this use case, tests demonstrate the high accuracy of the modeling framework in predicting the upcoming services of vacant taxis. We further analyze the factors affecting the predictive accuracy via a prediction accuracy analysis and prediction location evaluation. The findings of this paper can provide intelligence for the improvement of taxi services, to increase the passenger capacity of taxis and also to improve the probability of passengers finding taxis.

scholarly journals Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks

2020 ◽  
Author(s):  
Joshua Levy ◽  
Christian Haudenschild ◽  
Clark Barwick ◽  
Brock Christensen ◽  
Louis Vaickus
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Predictive Accuracy ◽  
Topological Data Analysis ◽  
Modeling Framework ◽  
Thin Sections ◽  
Essential Components ◽  
Level Information ◽  
Graph Neural Networks ◽  
Whole Slide Images

Whole-slide images (WSI) are digitized representations of thin sections of stained tissue from various patient sources (biopsy, resection, exfoliation, fluid) and often exceed 100,000 pixels in any given spatial dimension. Deep learning approaches to digital pathology typically extract information from sub-images (patches) and treat the sub-images as independent entities, ignoring contributing information from vital large-scale architectural relationships. Modeling approaches that can capture higher-order dependencies between neighborhoods of tissue patches have demonstrated the potential to improve predictive accuracy while capturing the most essential slide-level information for prognosis, diagnosis and integration with other omics modalities. Here, we review two promising methods for capturing macro and micro architecture of histology images, Graph Neural Networks, which contextualize patch level information from their neighbors through message passing, and Topological Data Analysis, which distills contextual information into its essential components. We introduce a modeling framework, WSI-GTFE that integrates these two approaches in order to identify and quantify key pathogenic information pathways. To demonstrate a simple use case, we utilize these topological methods to develop a tumor invasion score to stage colon cancer.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

scholarly journals Social Collective Attack Model and Procedures for Large-Scale Cyber-Physical Systems

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 991
Author(s):  
Peidong Zhu ◽  
Peng Xun ◽  
Yifan Hu ◽  
Yinqiao Xiong
Keyword(s):  
Smart Grid ◽  
Collective Behavior ◽  
Physical System ◽  
Large Scale ◽  
Public Utility ◽  
Modeling Framework ◽  
Vast Number ◽  
Attack Model ◽  
The Social ◽  
Social Collective

A large-scale Cyber-Physical System (CPS) such as a smart grid usually provides service to a vast number of users as a public utility. Security is one of the most vital aspects in such critical infrastructures. The existing CPS security usually considers the attack from the information domain to the physical domain, such as injecting false data to damage sensing. Social Collective Attack on CPS (SCAC) is proposed as a new kind of attack that intrudes into the social domain and manipulates the collective behavior of social users to disrupt the physical subsystem. To provide a systematic description framework for such threats, we extend MITRE ATT&CK, the most used cyber adversary behavior modeling framework, to cover social, cyber, and physical domains. We discuss how the disinformation may be constructed and eventually leads to physical system malfunction through the social-cyber-physical interfaces, and we analyze how the adversaries launch disinformation attacks to better manipulate collective behavior. Finally, simulation analysis of SCAC in a smart grid is provided to demonstrate the possibility of such an attack.

scholarly journals Finite cell method for functionally graded materials based on V-models and homogenized microstructures

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Benjamin Wassermann ◽  
Nina Korshunova ◽  
Stefan Kollmannsberger ◽  
Ernst Rank ◽  
Gershon Elber
Keyword(s):  
Functionally Graded Materials ◽  
Large Scale ◽  
Functionally Graded ◽  
Material Behavior ◽  
Modeling Framework ◽  
Finite Cell Method ◽  
Geometric Framework ◽  
Graded Materials ◽  
Cell Method ◽  
Finite Cell

AbstractThis paper proposes an extension of the finite cell method (FCM) to V-rep models, a novel geometric framework for volumetric representations. This combination of an embedded domain approach (FCM) and a new modeling framework (V-rep) forms the basis for an efficient and accurate simulation of mechanical artifacts, which are not only characterized by complex shapes but also by their non-standard interior structure. These types of objects gain more and more interest in the context of the new design opportunities opened by additive manufacturing, in particular when graded or micro-structured material is applied. Two different types of functionally graded materials (FGM) are considered: The first one, multi-material FGM is described using the inherent property of V-rep models to assign different properties throughout the interior of a domain. The second, single-material FGM—which is heterogeneously micro-structured—characterizes the effective material behavior of representative volume elements by homogenization and performs large-scale simulations using the embedded domain approach.

scholarly journals On the geophysical processes impacting palaeo-sea-level observations

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yusuke Yokoyama ◽  
Anthony Purcell
Keyword(s):  
Sea Level ◽  
Large Scale ◽  
Global Climate ◽  
Sea Level Change ◽  
Sea Level Changes ◽  
Factors Affecting ◽  
Ice Volume ◽  
Level Change ◽  
Recent Developments ◽  
Geophysical Processes

AbstractPast sea-level change represents the large-scale state of global climate, reflecting the waxing and waning of global ice sheets and the corresponding effect on ocean volume. Recent developments in sampling and analytical methods enable us to more precisely reconstruct past sea-level changes using geological indicators dated by radiometric methods. However, ice-volume changes alone cannot wholly account for these observations of local, relative sea-level change because of various geophysical factors including glacio-hydro-isostatic adjustments (GIA). The mechanisms behind GIA cannot be ignored when reconstructing global ice volume, yet they remain poorly understood within the general sea-level community. In this paper, various geophysical factors affecting sea-level observations are discussed and the details and impacts of these processes on estimates of past ice volumes are introduced.

scholarly journals Digital proximity tracing on empirical contact networks for pandemic control

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
G. Cencetti ◽  
G. Santin ◽  
A. Longa ◽  
E. Pigani ◽  
A. Barrat ◽  
...  
Keyword(s):  
Large Scale ◽  
Disease Outbreaks ◽  
Physical Distance ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Modeling Framework ◽  
Infectious Disease Outbreaks ◽  
Contact Patterns ◽  
Contact Data ◽  
The Impact

AbstractDigital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15–20 minutes and closer than 2–3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide.

What Are the Factors Affecting Total Sleep Time During Video Polysomnography in Infants?

2020 ◽  
Author(s):  
Grace R. Paul ◽  
Don Hayes ◽  
Dmitry Tumin ◽  
Ish Gulati ◽  
Sudarshan Jadcherla ◽  
...  
Keyword(s):  
Total Sleep Time ◽  
Sleep Time ◽  
Time Of Day ◽  
Apnea Hypopnea Index ◽  
Clinical Factors ◽  
Initial Management ◽  
Factors Affecting ◽  
Modifiable Factors ◽  
Key Points ◽  
Medical Complexity

Objective The aim of the study is to investigate factors affecting total sleep time (TST) during infant polysomnography (PSG) and assess if <4 hours of TST is sufficient for accurate interpretation. Study Design Overall, 242 PSGs performed in 194 infants <6 months of chronological age between March 2013 and December 2015 were reviewed to identify factors that affect TST, including age of infant, location and timing of study, presence of medical complexity, and presence of nasal tubes. A continuum of apnea-hypopnea index (AHI) in relation to TST was reviewed. Data were examined in infants who had TST <4 hours and low AHI. Results Greater TST (p < 0.001) was noted among infants during nocturnal PSGs, at older chronological and post-menstrual ages, and without medical complexity. The presence of nasogastric/impedance probes reduced TST (p = 0.002). Elevated AHIs were identified even in PSGs with TST <4 hours. Short TST may have affected interpretation and delayed initial management in one infant without any inadvertent complications. Conclusion Clinical factors such as PMA and medical complexity, and potentially modifiable factors such as time of day and location of study appeared to affect TST during infant PSGs. TST < 4 hours can be sufficient to identify high AHI allowing physician interpretation. Key Points

scholarly journals Progress in Research on Carbon Nanotubes Reinforced Cementitious Composites

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Qinghua Li ◽  
Jintao Liu ◽  
Shilang Xu
Keyword(s):  
Carbon Nanotubes ◽  
Large Scale ◽  
High Strength ◽  
Cementitious Composites ◽  
Scale Production ◽  
Multiwalled Carbon ◽  
Factors Affecting ◽  
Environment And Health ◽  
Research Activities ◽  
Major Factors

As one-dimensional (1D) nanofiber, carbon nanotubes (CNTs) have been widely used to improve the performance of nanocomposites due to their high strength, small dimensions, and remarkable physical properties. Progress in the field of CNTs presents a potential opportunity to enhance cementitious composites at the nanoscale. In this review, current research activities and key advances on multiwalled carbon nanotubes (MWCNTs) reinforced cementitious composites are summarized, including the effect of MWCNTs on modulus of elasticity, porosity, fracture, and mechanical and microstructure properties of cement-based composites. The issues about the improvement mechanisms, MWCNTs dispersion methods, and the major factors affecting the mechanical properties of composites are discussed. In addition, large-scale production methods of MWCNTs and the effects of CNTs on environment and health are also summarized.

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