Results 41 to 50 of about 3,774,487 (407)
Human Travel and Traveling Bedbugs [PDF]
Journal of Travel Medicine, 2012 A dramatic increase of reported bedbug (Cimex lectularius and Cimex hemipterus) infestations has been observed worldwide over the past decade. Bedbug infestations have also been detected across a wide range of travel accommodations, regardless of their comfort and hygiene levels.
openaire +3 more sources
Environmental Contribution to the Effectiveness of Food Security Policy Implementation as A Rehabilitation Effort for Stunting Treatment in Rokan Hilir Regency [PDF]
E3S Web of ConferencesAccording to SDGs 2030, food security must have 3 principles, namely; Availability, Affordability; and Benefits, hereinafter referred to as food security aspects. Rokan Hilir is an area with a high level of stunting. Referring to the problem formulation,
Yuliani Febri +2 more
doaj +1 more source
Assessment of Luxury Trains in India: A Case Study of Maharajas’ Express
Journal of Tourismology, 2020 With Indian luxury trains recording a low rate of occupancy, it has become imperative to understand the perceptions of its service providers and consumers.
Jeet Dogra, Venkata Rohan Sharma Karri
doaj +1 more source
Multi-view user representation learning for user matching without personal information [PDF]
, 2023 As the digitization of travel industry accelerates, analyzing and understanding travelers' behaviors becomes increasingly important. However, traveler data frequently exhibit high data sparsity due to the relatively low frequency of user interactions with travel providers.
arxiv +1 more source
The scaling laws of human travel [PDF]
Nature, 2006 The dynamic spatial redistribution of individuals is a key driving force of various spatiotemporal phenomena on geographical scales. It can synchronize populations of interacting species, stabilize them, and diversify gene pools.
D. Brockmann, L. Hufnagel, T. Geisel
semanticscholar +1 more source
Comparative Analysis of Machine Learning Models for Predicting Travel Time [PDF]
arXiv, 2021 In this paper, five different deep learning models are being compared for predicting travel time. These models are autoregressive integrated moving average (ARIMA) model, recurrent neural network (RNN) model, autoregressive (AR) model, Long-short term memory (LSTM) model, and gated recurrent units (GRU) model.
arxiv
International Journal of Environmental Research and Public Health, 2020 The impacts of COVID-19 are massive. Global tourism is one of the industries that is heavily affected. “Travel bubble”, a recent term initiated by travel operators, is a programme that allows tourists to travel to countries nearby without quarantine ...
J. Luo, Chi Fung Lam
semanticscholar +1 more source
Travel survey data required to inform transport safety policy and practice [PDF]
, 2005 The risk of accidental death per hour spent using the roads in Hong Kong is about I I times the average risk per hour in the rest of everyday life. Other kinds of travel also have risks.
Allsop, RE
core +1 more source
Интеллект. Инновации. Инвестиции, 2023 The relevance of this article is dictated by the need to study ways to improve the efficiency of trade through the introduction of innovative systems of customs management and control.
I. Podlesny
doaj +1 more source
Helioseismic Travel-Time Definitions and Sensitivity to Horizontal Flows Obtained From Simulations of Solar Convection [PDF]
, 2009 We study the sensitivity of wave travel times to steady and spatially homogeneous horizontal flows added to a realistic simulation of the solar convection performed by Robert F. Stein, Ake Nordlund, Dali Georgobiani, and David Benson. Three commonly used definitions of travel times are compared.
arxiv +1 more source