Human Travel Research Articles

Development of a machine learning framework for radiation biomarker discovery and absorbed dose prediction.

Molecular radiation biomarkers are an emerging tool in radiation research with applications for cancer radiotherapy, radiation risk assessment, and even human space travel. However, biomarker screening in genome-wide expression datasets using conventional tools is time-consuming and underlies analyst (human) bias. Machine Learning (ML) methods can improve the sensitivity and specificity of biomarker identification, increase analytical speed, and avoid multicollinearity and human bias. To develop a resource-efficient ML framework for radiation biomarker discovery using gene expression data from irradiated normal tissues. Further, to identify biomarker panels predicting radiation dose with tissue specificity. A strategic search in the Gene Expression Omnibus database identified a transcriptomic dataset (GSE44762) for normal tissues radiation responses (murine kidney cortex and medulla) suited for biomarker discovery using an ML approach. The dataset was pre-processed in R and separated into train and test data subsets. High computational cost of Genetic Algorithm/k-Nearest Neighbor (GA/KNN) mandated optimization and 13 ML models were tested using the caret package in R. Biomarker performance was evaluated and visualized via Principal Component Analysis (PCA) and dose regression. The novelty of ML-identified biomarker panels was evaluated by literature search. Caret-based feature selection and ML methods vastly improved processing time over the GA approach. The KNN method yielded overall best performance values on train and test data and was implemented into the framework. The top-ranking genes were Cdkn1a, Gria3, Mdm2 and Plk2 in cortex, and Brf2, Ccng1, Cdkn1a, Ddit4l, and Gria3 in medulla. These candidates successfully categorized dose groups and tissues in PCA. Regression analysis showed that correlation between predicted and true dose was high with R2 of 0.97 and 0.99 for cortex and medulla, respectively. The caret framework is a powerful tool for radiation biomarker discovery optimizing performance with resource-efficiency for broad implementation in the field. The KNN-based approach identified Brf2, Ddit4l, and Gria3 mRNA as novel candidates that have been uncharacterized as radiation biomarkers to date. The biomarker panel showed good performance in dose and tissue separation and dose regression. Further training with larger cohorts is warranted to improve accuracy, especially for lower doses.

A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility.

The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. A major challenge in forecasting the transmission of COVID-19 is the accurate assessment of the multiscale human mobility and how it impacts infection through close contacts. By combining the stochastic agent-based modeling strategy and hierarchical structures of spatial containers corresponding to the notion of geographical places, this study proposes a novel model, Mob-Cov, to study the impact of human traveling behavior and individual health conditions on the disease outbreak and the probability of zero-COVID in the population. Specifically, individuals perform power law-type local movements within a container and global transport between different-level containers. It is revealed that frequent long-distance movements inside a small-level container (e.g., a road or a county) and a small population size reduce both the local crowdedness and disease transmission. It takes only half of the time to induce global disease outbreaks when the population increases from 150 to 500 (normalized unit). When the exponent of the long-tail distribution of distance k moved in the same-level container, , increases, the outbreak time decreases rapidly from 75 to 25 (normalized unit). In contrast, travel between large-level containers (e.g., cities and nations) facilitates global spread of the disease and outbreak. When the mean traveling distance across containers increases from 0.5 to 1 (normalized unit), the outbreak occurs almost twice as fast. Moreover, dynamic infection and recovery in the population are able to drive the bifurcation of the system to a "zero-COVID" state or to a "live with COVID" state, depending on the mobility patterns, population number and health conditions. Reducing population size and restricting global travel help achieve zero-COVID-19. Specifically, when is smaller than 0.2, the ratio of people with low levels of mobility is larger than 80% and the population size is smaller than 400, zero-COVID can be achieved within fewer than 1000 time steps. In summary, the Mob-Cov model considers more realistic human mobility at a wide range of spatial scales, and has been designed with equal emphasis on performance, low simulation cost, accuracy, ease of use and flexibility. It is a useful tool for researchers and politicians to apply when investigating pandemic dynamics and when planning actions against disease. The online version contains supplementary material available at 10.1007/s11071-023-08489-5.

Resilience patterns of human mobility in response to extreme urban floods.

Large-scale disasters can disproportionately impact different population groups, causing prominent disparity and inequality, especially for the vulnerable and marginalized. Here, we investigate the resilience of human mobility under the disturbance of the unprecedented '720' Zhengzhou flood in China in 2021 using records of 1.32 billion mobile phone signaling generated by 4.35 million people. We find that although pluvial floods can trigger mobility reductions, the overall structural dynamics of mobility networks remain relatively stable. We also find that the low levels of mobility resilience in female, adolescent and older adult groups are mainly due to their insufficient capabilities to maintain business-as-usual travel frequency during the flood. Most importantly, we reveal three types of counter-intuitive, yet widely existing, resilience patterns of human mobility (namely, 'reverse bathtub', 'ever-increasing' and 'ever-decreasing' patterns), and demonstrate a universal mechanism of disaster-avoidance response by further corroborating that those abnormal resilience patterns are not associated with people's gender or age. In view of the common association between travel behaviors and travelers' socio-demographic characteristics, our findings provide a caveat for scholars when disclosing disparities in human travel behaviors during flood-induced emergencies.

Modifying the Environment or Human Nature? What is the Right Choice for Space Travel and Mars Colonisation?

As space travel and intentions to colonise other planets are becoming the norm in public debate and scholarship, we must also confront the technical and survival challenges that emerge from these hostile environments. This paper aims to evaluate the various arguments proposed to meet the challenges of human space travel and extraterrestrial planetary colonisation. In particular, two primary solutions have been present in the literature as the most straightforward solutions to the rigours of extraterrestrial survival and flourishing: (1) geoengineering, where the environment is modified to become hospitable to its inhabitants, and (2) human (bio)enhancement where the genetic heritage of humans is modified to make them more resilient to the difficulties they may encounter as well as to permit them to thrive in non-terrestrial environments. Both positions have strong arguments supporting them but also severe philosophical and practical drawbacks when exposed to different circumstances. This paper aims to show that a principled stance where one position is accepted wholesale necessarily comes at the opportunity cost of the other where the other might be better suited, practically and morally. This paper concludes that case-by-case evaluations of the solutions to space travel and extraterrestrial colonisation are necessary to ensure moral congruency and the survival and flourishing of astronauts now and into the future.

Ecological aspects of volatile organic compounds emitted by exotic invasive plants

Exotic invasive plants are present in almost all terrestrial ecosystems. Their spread and ability to colonize new habitats are predicted to increase with human travel, global trade, and climate change. These plants alter the environments they invade in multiple ways, affecting surrounding species. Chemically, invasive plants can modify their environment by releasing secondary metabolites such as root exudates (liquid) or airborne (gaseous) volatile organic compounds (VOCs). The allelopathic effect of invasive species' root exudates is well studied and acknowledged as a trait contributing to invasiveness. However, less is known about the effects of invasive species' VOCs, which are likely to play important ecological roles. Therefore, we conducted a systematic review of the literature during the last decade (2012–2022) to explore what is known about the ecological aspects of VOCs emitted by invasive plants, focusing on the factors affecting their emission (genetic, biotic, and abiotic), and their role on plant-plant and plant-insect interactions. We found 29 studies matching our search criteria. These studies suggest that invasive species are more “chemically diverse” than their native counterparts and have different chemical behavior in native compared to invaded ranges. The studies further highlight that chemical traits are heritable and contribute to invasiveness. Multiple biotic and abiotic factors affecting invasive plants' VOC emission have been explored (e.g., herbivory, soil microorganisms, warming, and CO2). The studies indicate that invasive plants may experience less variation in their VOC emissions in response to environmental change than natives, with trade-offs between growth, reproduction and defense influencing VOC emissions. Regarding the impact on native species and their interactions, the allelopathic role of invasive plants' VOCs on native plants is well documented, consistently showing phytotoxic effects. There is also evidence of their involvement in neighbor detection. While volatile-mediated interactions between invasive plants and native insects remain poorly studied, the existing evidence shows that invasive plant VOCs have the potential to disrupt communication between native plants and insects. But insects also use multiple cues to make foraging/oviposition decisions, compensating for the disruptive effect of invasive plant VOCs. To conclude, we identify knowledge gaps and propose avenues for future research.

Assessing the mobility of Bronze Age societies in East-Central Europe. A strontium and oxygen isotope perspective on two archaeological sites.

European Bronze Age societies are generally characterised by increased mobility and the application of isotopic methods to archaeology has allowed the rate and range of human travels to be quantified. However, little is known about the mobility of the people inhabiting East-Central Europe in the late Early and Middle Bronze Age (1950-1250 BC) whose primary subsistence strategy was herding supported by crop cultivation. This paper presents the results of strontium (87Sr/86Sr) and oxygen (δ18O) isotope analyses in the enamel of people buried in collective graves at the cemeteries in Gustorzyn and Żerniki Górne. These sites are located in Kujawy and the Nida Basin, a lowland and an upland region with clearly different environmental conditions, respectively. Both sites are classified as belonging to the Trzciniec cultural circle and were used between 16th and 13th centuries BC. Among the 34 examined individuals only an adult female from Gustorzyn can be assessed as non-local based on both 87Sr/86Sr and δ18O signatures in her first molar. This may indicate the practice of exogamy in the studied population but more generally corresponds with the hypothesis of limited mobility within these societies, as has previously been inferred from archaeological evidence, anthropological analysis, and stable isotope-based diet reconstruction. New and existing data evaluated in this paper show that the 87Sr/86Sr variability in the natural environment of both regions is relatively high, allowing the tracking of short-range human mobility. A series of oxygen isotope analyses (conducted for all but one individuals studied with strontium isotopes) indicates that δ18O ratios measured in phosphate are in agreement with the predicted modern oxygen isotope precipitation values, and that this method is useful in detecting travels over larger distances. The challenges of using both 87Sr/86Sr and δ18O isotopic systems in provenance studies in the glacial landscapes of temperate Europe are also discussed.

Role of recommendation sizes and travel involvement in evaluating travel destination recommendation services: comparison between artificial intelligence and travel experts

研究目的本研究旨在了解旅行者如何根据推荐的规模和他们的旅行参与度来评估从人工智能 (AI) 或人类旅行专家 (TE) 收到的旅行目的地推荐。研究设计/方法/途径本研究使用 2（代理类型：AI 与 TE）×2（推荐数量：小与大）×2（旅行参与：低与高）受试者间设计。调查结果当 AI 推荐目的地时, 参与度较低的旅行者认为推荐更可信, 并且当 AI 提供的建议比较小的建议大时信任系统。 同时, 当 TE 提供推荐时, 无论推荐数量大小和旅行参与度如何, 旅行者都认为这些推荐同样可信并且同样信任系统。研究原创性这项研究揭示了以人为本的人工智能旅游目的地推荐服务的设计。

Changes on local travel behaviors under travel reduction-related interventions during COVID-19 pandemic: a case study in Hong Kong

The emerging Omicron variant poses a serious threat to human health. Public transports play a critical role in infection spread. Based on the data of nearly 4 billion smartcard uses, between January 1, 2019 and January 31, 2021 from the Mass Transit Railway Corporation of Hong Kong, we analyzed the subway travel behavior of different population groups (adults, children, students and senior citizens) due to the COVID-19 pandemic and human travel behavior under different interventions (e.g. work suspension, school closure). Due to the pandemic, the number of MTR passengers (the daily number of passengers in close proximity in subway carriages) decreased by 37.4% (40.8%) for adults, 80.3% (78.5%) for children, 71.6% (71.6%) for students, and 33.5% (36.1%) for senior citizens. Due to work from home (school suspension), the number of contacted adults (students/children) in the same carriage during the rush hours decreased by 39.6% (38.6%/43.2%). If all workers, students, and children were encouraged to commute avoiding rush hours, the possible repeated contacts during rush hour of adults, children and students decreased by 73.3%, 77.9% and 79.5%, respectively. Since adults accounted for 87.3% of the total number of subway passengers during the pandemic, work from home and staggered shift pattern of workers can reduce the infection risk effectively. Our objective is to find the changes of local travel behavior due to the pandemic. From the perspective of public transports, the results provide a scientific support for COVID-19 prevention and control in cities.

Graph Supported Mode Detection within Mobile Phone Data Trajectories

Mobile phone data (MPD) has been used in various studies to analyze human travel behavior in time and space. While the pure number of available trips is promising, data quality and the information density of each individual trajectory is relatively low. Travel mode detection of MPD-trajectories is a challenging task, since geographic location data is noisy and the events within a trajectory are irregular (event-based instead of time discrete). In this paper, we present a method to identify travel modes typically observed in urban areas, such as walking, bicycle, tram, bus, and car. The method requires concise network graphs for each mode. Annotated GPS trajectories were collected from volunteers as ground truth to train and validate various machine learning algorithms. The cleaned trajectories of the MPD are segmented into individual trips, which are mapped on the network graphs using a map-matching algorithm. Various features, such as trip distance and travel speed, were analyzed to identify the most suitable features for classifying the available modes with a random forest (RF) and a support-vector machine algorithm. With the RF algorithm, about 80% of all trips were associated to the correct mode. Since the total dataset was only comprised of about 600 trips, which then needed to be split into an evaluation dataset and a training dataset, we suspect the accuracy of the method will increase with more data. Based on the results, this work presents a proof of concept for determining the travel mode of MPD-trajectories.

Design and Application of Intelligent Transportation Multi-Source Data Collaboration Framework Based on Digital Twins

The increasing urban traffic problems have made the transportation system require a large amount of data. Aiming at the current problems of data types redundancy and low coordination rate of intelligent transportation systems (ITS), this paper proposes an improved digital twin architecture applicable to ITS. Based on the improved digital twin architecture, a framework for dynamic and static data collaboration in ITS is constructed. For various collaboration methods, this paper specifically describes the collaboration methods and scopes, and designs the framework and interfaces for data mapping. Finally, the effectiveness of the framework is verified by case studies to mine the spatiotemporal distribution characteristics of data, capture human travel characteristics, and visualize intersections using digital twins. This paper provides a new data fusion idea for digital twin systems in ITS, and the framework covers all data types in digital twin systems for cross-integration analysis.

Local travel behaviour under continuing COVID-19 waves– A proxy for pandemic fatigue?

COVID-19 continues to threaten the world. Relaxing local travel behaviours on preventing the spread of COVID-19, may increase the infection risk in subsequent waves of SARS-CoV-2 transmission. In this study, we analysed changes in the travel behaviour of different population groups (adult, child, student, elderly) during four pandemic waves in Hong Kong before January 2021, by 4-billion second-by-second smartcard records of subway. A significant continuous relaxation in human travel behaviour was observed during the four waves of SARS-CoV-2 transmission. Residents sharply reduced their local travel by 51.9%, 50.1%, 27.6%, and 20.5% from the first to fourth pandemic waves, respectively. The population flow in residential areas, workplaces, schools, shopping areas, amusement areas and border areas, decreased on average by 30.3%, 33.5%, 41.9%, 58.1%, 85.4% and 99.6%, respectively, during the pandemic weeks. We also found that many other cities around the world experienced a similar relaxation trend in local travel behaviour, by comparing traffic congestion data during the pandemic with data from the same period in 2019. The quantitative pandemic fatigue in local travel behaviour could help governments partially predicting personal protective behaviours, and thus to suggest more accurate interventions during subsequent waves, especially for highly infectious virus variants such as Omicron.

Future Health: Leveraging Space for Health Innovation

Mr. James Hury, Deputy Director and Chief Innovation Officer for the Translational Research Institute for Space Health (TRISH) explores the future of health, and how preparing technologies to protect the health of astronauts in deep space can advance health for all humans, wherever they explore. Supported by the NASA Human Research Program (HRP), TRISH is an applied space health research catalyst that funds disruptive, high-impact scientific studies and technologies to equip astronauts for deep space exploration. The institute is seeking bold, courageous proposals from experts in disciplines spanning medicine, science, technology, and engineering who have the ideas and skills that will help us find solutions for the challenges and limitations facing human space travel.

Innovative and Competitive: A Systematic Literature Review on New Tourism Destinations and Products for Tourism Supply

New tourism destinations and product supply are increasingly popular strategies among industry players to create competitiveness. The tourism industry has become more vibrant since globalization merged with technological changes, thus influencing human travel movements, lifestyles, and current trends. The purpose of the study was to systematically review and analyse the elements of new tourism destination products and the importance of sustaining the tourism industry. The following five primary methodological steps were used in the review processes: review protocol; formulation of research questions; systematic searching strategies based on identification, screening, and eligibility on Web of Science (WoS) and Scopus databases; quality appraisal and data extraction; analysis. Seven major themes were discovered, based on the thematic analysis: (a) Responsibilities and involvement, (b) Geography and resources, (c) Innovation, (d) Diversification, (e) Trend and travel pattern, (f) Sustainability, and (g) Marketing. The findings provide academics with a basic reference in scientific and design-related frameworks for examining new destinations and tourism products. The study also offers a guide for destination operators and industry stakeholders to be adopted for future destination development and management.

SOSIALISASI PENDIDIKAN KEPARIWISATAAN KEPADA GURU-GURU PENDIDIKAN ANAK USIA DINI (PAUD) GUGUS JAHAYA JINGGA KAB. BINTAN

The phenomenon of tourism has existed since the existence of human travel from one place to another and its development is in accordance with the socio-cultural community itself. Since then there have also been human needs that must be met during the journey, in addition to the motivation that drives humans to fulfill their needs. With the increase in human civilization, the urge to travel is getting stronger and the needs to be fulfilled are increasingly complex. The motivations and motives for traveling from time to time are of different levels, according to the development and socio-cultural, economic and environmental level of the community itself. The motivations and motives of people's journeys in prehistoric times are different from the motivations and motives of people's journeys in modern times. The way of travel and the facilities used by the community are still simple when compared to more advanced societies.

The Healthcare of the Final Frontier—Comprehensive Epidemiological Approach to the Future of Human Space Exploration and Travel

Today, the discussion about colonizing different planets has shifted from the “IF” mode to the “WHEN” mode, and the substantially large group of humans living for a long time in an isolated, hostile, and enclosed environment is about to become a reality. Thus, the space-related healthcare methodologies should shift as well from a small number of selected persons to much more extensive groups with much less health homogeneity. Moreover, the development of human colonies, first on the Moon and later on Mars, will be a game changer in all that we know and use to see in space travel and eventually in space health. Humans will leave Earth but will carry human society’s health patterns, adjusted to the environment. The foundation for space epidemiology modeling can be set today and should attract the attention of the epidemiological and space research communities.

Spatial-temporal diffusion model of aggregated infectious diseases based on population life characteristics: a case study of COVID-19.

Outbreaks of infectious diseases pose significant threats to human life, and countries around the world need to implement more precise prevention and control measures to contain the spread of viruses. In this study, we propose a spatial-temporal diffusion model of infectious diseases under a discrete grid, based on the time series prediction of infectious diseases, to model the diffusion process of viruses in population. This model uses the estimated outbreak origin as the center of transmission, employing a tree-like structure of daily human travel to generalize the process of viral spread within the population. By incorporating diverse data, it simulates the congregation of people, thus quantifying the flow weights between grids for population movement. The model is validated with some Chinese cities with COVID-19 outbreaks, and the results show that the outbreak point estimation method could better estimate the virus transmission center of the epidemic. The estimated location of the outbreak point in Xi'an was only 0.965 km different from the actual one, and the results were more satisfactory. The spatiotemporal diffusion model for infectious diseases simulates daily newly infected areas, which effectively cover the actual patient infection zones on the same day. During the mid-stage of viral transmission, the coverage rate can increase to over 90%, compared to related research, this method has improved simulation accuracy by approximately 18%. This study can provide technical support for epidemic prevention and control, and assist decision-makers in developing more scientific and efficient epidemic prevention and control policies.

A review on monkeypox virus outbreak: New challenge for world.

Monkeypox is a viral zoonotic disease caused by the monkeypox virus, a double-stranded DNA-enveloped virus that can be transmitted from animal to human or human to human. Consequently, it emerged as the most important orthopoxvirus for public health. Based on available online literature, this study reviewed the majority of the data representing the outbreak, diagnosis, treatment, and prevention of monkeypox. The literature search was conducted between July5 and September15, 2022. In addition to reviewing the databases of World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), Africa CDC, and United Kingdom Health Security Agency monkey pox advice, 43 papers were studied in depth. Human monkeypox was first identified in 1970 in a child in the Democratic Republic of the Congo. Until May 6, 2022, it was endemic in West and Central African countries and infrequently occurred outside of Africa. However, many cases have been identified in several nonendemic countries since May13, 2022, with no prior human or animal travel from endemic areas; that was the first time to document the cases and long-term transmission in countries with no epidemiological ties to endemic African countries. Seven travel-related human monkeypox cases were recorded outside of Africa from September 2018 to November 2021: one in Israel, one in Singapore, and two in the US Youth are most affected. Monkeypox's unanticipated development in places with no known epidemiological linkages raises concerns about the virus's evolution, which permits undetected transmission for a long period. Monkeypox is no longer a rare, self-limiting disease limited to endemic countries. Its ever-changing epidemiology and transmission dynamics have increased the possibility of its evolving into a much deadlier pathogen. Therefore, improved surveillance and detailed case and contact investigation are required to comprehend the ever-changing epidemiology of monkeypox.

Understanding the Biology and Immune Pathogenesis of Chikungunya Virus Infection for Diagnostic and Vaccine Development.

Chikungunya virus, the causative agent of chikungunya fever, is generally characterized by the sudden onset of symptoms, including fever, rash, myalgia, and headache. In some patients, acute chikungunya virus infection progresses to severe and chronic arthralgia that persists for years. Chikungunya infection is more commonly identified in tropical and subtropical regions. However, recent expansions and epidemics in the temperate regions have raised concerns about the future public health impact of chikungunya diseases. Several underlying factors have likely contributed to the recent re-emergence of chikungunya infection, including urbanization, human travel, viral adaptation to mosquito vectors, lack of effective control measures, and the spread of mosquito vectors to new regions. However, the true burden of chikungunya disease is most likely to be underestimated, particularly in developing countries, due to the lack of standard diagnostic assays and clinical manifestations overlapping with those of other endemic viral infections in the regions. Additionally, there have been no chikungunya vaccines available to prevent the infection. Thus, it is important to update our understanding of the immunopathogenesis of chikungunya infection, its clinical manifestations, the diagnosis, and the development of chikungunya vaccines.

Human Health during Space Travel: State-of-the-Art Review

The field of human space travel is in the midst of a dramatic revolution. Upcoming missions are looking to push the boundaries of space travel, with plans to travel for longer distances and durations than ever before. Both the National Aeronautics and Space Administration (NASA) and several commercial space companies (e.g., Blue Origin, SpaceX, Virgin Galactic) have already started the process of preparing for long-distance, long-duration space exploration and currently plan to explore inner solar planets (e.g., Mars) by the 2030s. With the emergence of space tourism, space travel has materialized as a potential new, exciting frontier of business, hospitality, medicine, and technology in the coming years. However, current evidence regarding human health in space is very limited, particularly pertaining to short-term and long-term space travel. This review synthesizes developments across the continuum of space health including prior studies and unpublished data from NASA related to each individual organ system, and medical screening prior to space travel. We categorized the extraterrestrial environment into exogenous (e.g., space radiation and microgravity) and endogenous processes (e.g., alteration of humans' natural circadian rhythm and mental health due to confinement, isolation, immobilization, and lack of social interaction) and their various effects on human health. The aim of this review is to explore the potential health challenges associated with space travel and how they may be overcome in order to enable new paradigms for space health, as well as the use of emerging Artificial Intelligence based (AI) technology to propel future space health research.

Exploring the Impact of Localized COVID-19 Events on Intercity Mobility during the Normalized Prevention and Control Period in China.

Uncontrolled, large-scale human mobility can amplify a localized disease into a pandemic. Tracking changes in human travel behavior, exploring the relationship between epidemic events and intercity travel generation and attraction under policies will contribute to epidemic prevention efforts, as well as deepen understanding of the essential changes of intercity interactions in the post-epidemic era. To explore the dynamic impact of small-scale localized epidemic events and related policies on intercity travel, a spatial lag model and improved gravity models are developed by using intercity travel data. Taking the localized COVID-19 epidemic in Xi'an, China as an example, the study constructs the travel interaction characterization before or after the pandemic as well as under constraints of regular epidemic prevention policies, whereby significant impacts of epidemic events are explored. Moreover, indexes of the quantified policies are refined to the city level in China to analyze their effects on travel volumes. We highlight the non-negligible impacts of city events and related policies on intercity interaction, which can serve as a reference for travel management in case of such severe events.