Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53

Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO2 emissions and non-CO2 impacts, such as NOx emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NOx emitted, or per flown kilometre.In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NOx-induced ozone and H2O effects are well represented by the ACCF model output. The NOx-induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NOx-induced ozone effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NOx-induced ozone compared to cost-optimized trajectories. Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO2 contribution observed in this study is low compared with the non-CO2 effects, which requires further diagnosis.

Contact Tracing over Uncertain Indoor Positioning Data

Pandemics often cause dramatic losses of human lives and impact our societies in many aspects such as public health, tourism, and economy. To contain the spread of an epidemic like COVID-19, efficient and effective contact tracing is important, especially in indoor venues where the risk of infection is higher. In this work, we formulate and study a novel query called Indoor Contact Query (<sc>ICQ</sc>) over raw, uncertain indoor positioning data that digitalizes people's movements indoors. Given a query object <inline-formula><tex-math notation="LaTeX">$o$</tex-math></inline-formula>, e.g., a person confirmed to be a virus carrier, an <sc>ICQ</sc> analyzes uncertain indoor positioning data to find objects that most likely had close contact with <inline-formula><tex-math notation="LaTeX">$o$</tex-math></inline-formula> for a long period of time. To process <sc>ICQ</sc>, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for <sc>ICQ</sc>. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and the acceleration strategies. We conduct extensive experiments on synthetic and real datasets to evaluate our proposals. The results demonstrate the efficiency and effectiveness of our proposals. IEEE

Change detection of travel behavior: a case study of COVID-19

Travel behavior is important in many fields, such as urban management and disaster management. Since the breakout of COVID-19, many people have changed their preference in travel, which is called travel behavior pattern, to respond to the impact of COVID-19. Understanding when, how, and why people change their travel behavior patterns is significant for antiepidemic and estimating the impact of COVID-19 on human society. However, most current studies ignore that travel behavior is multi-dimensions, and it can be a barrier to understanding travel behavior change. To fill up the vacuum of current research, we used an online Bayesian change detection method to detect individual travel behavior pattern change from big mobile trajectory data. For the low data quality problem caused by various and uneven, we design a novel Monte Carlo data grading framework to assess data quality and filter useable data and thus avoid unreliable results. The analysis result shows Tokyo experienced 6 phases of travel behavior change since 2020, and the change was driven by policies to some extent, especially in the frequency dimension and spatial dimension. Also, the correlation analysis indicates the correlation between four travel behavior dimension dimensions, and the infection number provides us with knowledge about how people will make a change in their travel behavior in the COVID-19 period. © 2023 Elsevier Inc. All rights reserved.

(Re)designing the continuum of care for older adults: The future of long-term care settings

This book broadens the visioning on new care environments that are designed to be inclusive, progressive, and convergent with the needs of an aging population. The contents cover a range of long-term care (LTC) settings in a single collection to address the needs of a wide audience. Due to the recent COVID-19 pandemic, rethinking the spatial design of care facilities in order to prepare for future respiratory and contagious pathogens is one of the prime concerns across the globe, along with social connectedness and autonomy in care settings. This book contributes to the next generation of knowledge and understanding of the growing field of the design of technology, programs, and environments for LTC that are more effective in infection prevention and control as well as social connectedness. To address these issues, the chapters are organized in four sections: Part I: Home- and community-based care;Part II: Facility-based care;Part III: Memory care and end-of-life care;and Part IV: Evidence-based applied projects and next steps. (Re)designing the Continuum of Care for Older Adults is an essential resource for researchers, practitioners, educators, policymakers, and students associated with LTC home and healthcare settings. With diverse topics in theory, substantive issues, and methods, the contributions from notable researchers and scholars cover a range of innovative programming, environments, and technologies which can impact the changing needs and support for older adults and their families across the continuum of care. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Characterization and Optimization of Trajectory Errors on Remote-Controlled Differential Drive Robot through Speed Calibration

An increase in interest in research projects which involves the design of robotic systems that minimizes interactions between humans has been caused by the COVID-19 outbreak, as such technology can greatly benefit healthcare industries in preventing the spread of highly infectious diseases. The utilization of remote-controlled robots in many different fields, especially in the medical field is becoming more and more necessary. However, mobile robots are susceptible to both systematic and nonsystematic errors that cause deviations in its trajectory. In view thereof, the researchers explored the possibility of minimizing the trajectory errors through speed calibration. The differential drive robot was navigated to finish a five-meter linear path of forward and backward motion. The test was conducted with a default linear speed of 0.5 m/s in which a high trajectory error was observed. Upon changing the speed of the robot, the same trajectory test was conducted at four additional different speeds, namely;0.25 m/s, 0.35 m/s, 0.65m/s and 0.75 m/s. With the gathered data, the researchers conducted a linear least-squares regression model using MATLAB wherein there is only one predictor variable (speed of the robot) and one response variable (deviation). Based on the results, the researchers concluded that the speed of 0.35 m/s is the optimal speed in which the trajectory error of the robot is minimal. The researchers recommend improving the design of the caster wheels to minimize the effects of nonsystematic errors. © 2022 IEEE.

Trajectory of Gastrointestinal Symptoms in Previously Hospitalized COVID-19 Survivors: The Long COVID Experience Multicenter Study.

This multicenter cohort study used Sankey plots and exponential bar plots to visualize the fluctuating evolution and the trajectory of gastrointestinal symptoms in previously hospitalized COVID-19 survivors during the first 18 months after acute SARS-CoV-2 infection. A total of 1266 previously hospitalized COVID-19 survivors were assessed at four points: hospital admission (T0), at 8.4 months (T1), at 13.2 months (T2), and at 18.3 months (T3) after hospitalization. Participants were asked about their overall gastrointestinal symptoms and particularly diarrhea. Clinical and hospitalization data were collected from hospital medical records. The prevalence of overall gastrointestinal post-COVID symptomatology was 6.3% (n = 80) at T1, 3.99% (n = 50) at T2 and 2.39% (n = 32) at T3. The prevalence of diarrhea decreased from 10.69% (n = 135) at hospital admission (T0), to 2.55% (n = 32) at T1, to 1.04% (n = 14) at T2, and to 0.64% (n = 8) at T3. The Sankey plots revealed that just 20 (1.59%) and 4 (0.32%) patients exhibited overall gastrointestinal post-COVID symptoms or diarrhea, respectively, throughout the whole follow-up period. The recovery fitted exponential curves revealed a decreasing prevalence trend, showing that diarrhea and gastrointestinal symptoms recover during the first two or three years after COVID-19 in previously hospitalized COVID-19 survivors. The regression models did not reveal any symptoms to be associated with the presence of gastrointestinal post-COVID symptomatology or post-COVID diarrhea at hospital admission or at T1. The use of Sankey plots revealed the fluctuating evolution of gastrointestinal post-COVID symptoms during the first two years after infection. In addition, exponential bar plots revealed the decreased prevalence of gastrointestinal post-COVID symptomatology during the first three years after infection.

Determining the role and responsibilities of the community epilepsy nurse in the management of epilepsy.

AIMS AND OBJECTIVES: The aim of this study is to enhance the understanding of the core elements and influencing factors on the community-based epilepsy nurse's role and responsibilities. BACKGROUND: Internationally, epilepsy nurse specialists play a key role in providing person-centred care and management of epilepsy but there is a gap in understanding of their role in the community. DESIGN: A national three-stage, mixed-method study was conducted. METHODS: One-on-one, in-depth semi-structured qualitative interviews were conducted online with 12 community-based epilepsy nurses (Stage 1); retrospective analysis of data collected from the National Epilepsy Line, a nurse-led community helpline (Stage 2); and focus group conducted with four epilepsy nurses, to delve further into emerging findings (Stage 3). A thematic analysis was conducted in Stages 1 and 3, and a descriptive statistical analysis of Stage 2 data. Consolidated Criteria for Reporting Qualitative studies checklist was followed for reporting. RESULTS: Three key themes emerged: (1) The epilepsy nurse career trajectory highlighted a lack of standardised qualifications, competencies, and career opportunities. (2) The key components of the epilepsy nurse role explored role diversity, responsibilities, and models of practice in the management of living with epilepsy, and experiences navigating complex fragmented systems and practices. (3) Shifting work practices detailed the adapting work practices, impacted by changing service demands, including COVID-19 pandemic experiences, role boundaries, funding, and resource availability. CONCLUSION: Community epilepsy nurses play a pivotal role in providing holistic, person-centred epilepsy management They contribute to identifying and addressing service gaps through innovating and implementing change in service design and delivery. RELEVANCE TO CLINICAL PRACTICE: Epilepsy nurses' person-centred approach to epilepsy management is influenced by the limited investment in epilepsy-specific integrated care initiatives, and their perceived value is impacted by the lack of national standardisation of their role and scope of practice. NO PATIENT OR PUBLIC CONTRIBUTION: Only epilepsy nurses' perspectives were sought.

Big data analytics and smart cities: applications, challenges, and opportunities.

Urban environments continuously generate larger and larger volumes of data, whose analysis can provide descriptive and predictive models as valuable support to inspire and develop data-driven Smart City applications. To this aim, Big data analysis and machine learning algorithms can play a fundamental role to bring improvements in city policies and urban issues. This paper introduces how Big Data analysis can be exploited to design and develop data-driven smart city services, and provides an overview on the most important Smart City applications, grouped in several categories. Then, it presents three real-case studies showing how data analysis methodologies can provide innovative solutions to deal with smart city issues. The first one is an approach for spatio-temporal crime forecasting (tested on Chicago crime data), the second one is methodology to discover mobility hotsposts and trajectory patterns from GPS data (tested on Beijing taxi traces), the third one is an approach to discover predictive epidemic patterns from mobility and infection data (tested on real COVID-19 data). The presented real-world cases prove that data analytics models can effectively support city managers in tackling smart city challenges and improving urban applications.

Optimal Path Control of a Patient for Rapid Infection Testing

In this paper, we propose a path control framework for guiding and simulating the patient's path of travel to speed up virus testing in pandemic situations, such as COVID-19. We use geographic information and hospital state information to construct graphs to yield optimal travel paths. Pathfinding algorithms A* and Navigation mesh, which have been widely used, are efficient when applied to control agents in a virtual environment. However, they are not suitable for real-time changing cases such as the COVID-19 environment because they guide only predetermined static routes. In order to receive a virus infection test quickly, there are many factors to consider, such as road traffic conditions, hospital size, number of patient movements, and patient processing time, in addition to guiding the shortest distance. In this paper, we propose a framework for digitally twinning various situations by modeling optimization functions considering various environmental factors in real-world urban maps to handle viral infection tests quickly and efficiently.

Beyond the Limits of Predictability in Human Mobility Prediction: Context-Transition Predictability

Urban human mobility prediction is forecasting how people move in cities. It is crucial for many smart city applications including route optimization, preparing for dramatic shifts in modes of transportation, or mitigating the epidemic spread of viruses such as COVID-19. Previous research propose the maximum predictability to derive the theoretical limits of accuracy that any predictive algorithm could achieve on predicting urban human mobility. However, existing maximum predictability only considers the sequential patterns of human movements and neglects the contextual information such as the time or the types of places that people visit, which plays an important role in predicting one's next location. In this paper, we propose new theoretical limits of predictability, namely Context-Transition Predictability, which not only captures the sequential patterns of human mobility, but also considers the contextual information of human behavior. We compare our Context-Transition Predictability with other kinds of predictability and find that it is larger than these existing ones. We also show that our proposed Context-Transition Predictability provides us a better guidance on which predictive algorithm to be used for forecasting the next location when considering the contextual information. Source code is at https://github.com/zcfinal/ContextTransitionPredictability.

A case study of academic acculturation of young students in Madagascar

Madagascar is going through a socio-economic slump resulting from the COVID-19 pandemic and the international economic crisis. Young graduates will have a major role to play in reversing this situation. The objective of this study is to identify the key parameters of the academic acculturation of young scholars and to better understand their acculturation model. Therefore, to innovate and adapt training programs and process to better train and prepare young people to face this development challenge in Madagascar. This case study was conducted in a higher education institution in the capital Antananarivo (18.8792° S, 47.5079° E). The study population is made up of young students. The purpose of the study is to analyze inductively the acculturation of these students during their academic program. The four hypotheses to be tested are (i) the behavioral acculturation of young students is a function of the exposure length, (ii) endogen characteristics influence the academic acculturation of students, and (iii) academic factors has an impact on academic acculturation. Acculturation is measured at two levels: behavior and adopted values which are the dependent variables. Personal situation, social reference, and academic factors are the independent variables whose importance in the acculturation of young people is the subject of this study. For this purpose, an exclusive questionnaire survey of the study population was conducted. The data obtained was subject to descriptive and factorial statistical analysis. This study led to two acculturation models and three acculturation process of young students. These findings in acculturation trajectories and models would be helpful for to innovate and well adapt the youth training program and the promotion of the transformational process of managerial practices and the socio-economic recovery of the Big Island. © 2023 Nova Science Publishers, Inc. All rights reserved.

Improvement of Trajectory Errors on Remote- Controlled Differential Drive Robot via Mobilebased GUI through Bluetooth Connection

There has been an increase of interest and demand in the usage of logistic indoor service robots that are designed to minimize interactions between humans due to the occurrence of the COVID-19 outbreak. The application of the rising technology in the medical sector has great benefits in the industry, such as the prevention of the spread of highly infectious diseases using distance as a factor. Rooting from the purpose of the said robot, the main focus should be the ease of navigation through achieving the desired trajectory, in order to maximize the functionality, prevent collision, reduce user maneuvering difficulties, and such. Hence, this paper is focused on improving the trajectory errors on the robot navigation performance based on different control system designs specifically, a physical joystick controller and a mobile-based Bluetooth application controller. The design of the joystick is based on a pivot as its base which is directed to all angles and the design of the Bluetooth app is based on fourdirectional buttons that will operate upon clicking, and switching to other buttons to change commands. With this, the researchers conducted linear path and rotational tests using both remote control modes that are based on five varying speed values of 0.75 m/s, 0.5m/s, 0.35m/s, 0.25m/s, and 0.15 m/s. Based on the data analysis, the yielded results showed that using the Bluetooth app lowers the robot's trajectory error by 50% to 60% compared to using ajoystick to navigate to the desired point. Thus, the researchers concluded that the design of a control system greatly affects the robot navigation in achieving the desired trajectory. Considering the nonsystematic errors, a calibration based on the hardware structure design specifically on the caster wheel is recommended. © 2023 IEEE.

How final year high school students' depression develop during COVID-19 in China? A latent class growth modeling analysis.

Depression increased sharply during the initial months of COVID-19, but how it developed over time is rarely explored, especially for adolescents. The current study measured depression of 605 final year high school students in China over 11 months in 4 waves. The latent growth curve modeling (LGCM) was used to examine overall trends in depression and latent class growth modeling (LCGM) was used to identify potential subgroups of adolescents' depressive trajectories. At the same time, gender, life events, and rumination were included as time-invariant covariates. Overall, the development of depression in the final year of high school students showed a slight downward trend. Meanwhile, the depression trajectories showed heterogeneity, and three categories of depression trajectories were identified, which were low-stable (24.3%), depression-risk (67.9%), and high-stable (7.8%). Neuroticism, rumination, and life events such as punishment and loss were found to significantly predict these trajectories of depression. This study helps to characterize differential depression trajectories among adolescents throughout the COVID-19 pandemic and establish several related predictors of the trajectory of depression.

Trajectories of the evolution of post-COVID-19 condition, up to two years after symptoms onset.

OBJECTIVES: We aimed to identify trajectories of the evolution of post-COVID-19 condition, up to 2 years after symptom onset. METHODS: The ComPaRe long COVID e-cohort is a prospective cohort of patients with symptoms lasting at least 2 months after SARS-CoV2 infection. We used trajectory modeling to identify different trajectories in the evolution of post-COVID-19 condition, based on symptoms collected every 60 days using the long COVID Symptom Tool. RESULTS: A total of 2197 patients were enrolled in the cohort between December 2020 and July 2022 when the Omicron variant was not dominant. Three trajectories of the evolution of post-COVID-19 condition were identified: "high persistent symptoms" (4%), "rapidly decreasing symptoms" (5%), and "slowly decreasing symptoms" (91%). Participants with highly persistent symptoms were older and more likely to report a history of systemic diseases. They often reported tachycardia, bradycardia, palpitations, and arrhythmia. Participants with rapidly decreasing symptoms were younger and more likely to report a confirmed infection. They often reported diarrhea and back pain. Participants with slowly decreasing symptoms were more likely to have a history of functional diseases. CONCLUSION: Most patients with post-COVID-19 condition improve slowly over time, while 5% have rapid improvement in the 2 years after symptom onset and 4% have a persistent condition.

Trajectory and Demographic Correlates of Antibodies to SARS-CoV-2 Nucleocapsid in Recently Infected Blood Donors, United States.

We evaluated antibodies to the nucleocapsid protein of SARS-CoV-2 in a large cohort of blood donors in the United States who were recently infected with the virus. Antibodies to the nucleocapsid protein of SARS-CoV-2 indicate previous infection but are subject to waning, potentially affecting epidemiologic studies. We longitudinally evaluated a cohort of 19,323 blood donors who had evidence of recent infection by using a widely available serologic test to determine the dynamics of such waning. We analyzed overall signal-to-cutoff values for 48,330 donations (average 2.5 donations/person) that had an average observation period of 102 days. The observed peak signal-to-cutoff value varied widely, but the waning rate was consistent across the range, with a half-life of 122 days. Within the cohort, only 0.75% of persons became seronegative. Factors predictive of higher peak values and longer time to seroreversion included increasing age, male sex, higher body mass index, and non-Caucasian race.

Design, Development, and Experimental Verification of a Trajectory Algorithm of a Telepresence Robot

Featured Application: Healthcare, remote sensing, and military. Background: Over the last few decades, telepresence robots (TRs) have drawn significant attention in academic and healthcare systems due to their enormous benefits, including safety improvement, remote access and economics, reduced traffic congestion, and greater mobility. COVID-19 and advancements in the military play a vital role in developing TRs. Since then, research on the advancement of robots has been attracting much attention. Methods: In critical areas, the placement and movement of humans are not safe, and researchers have started looking at the development of robots. Robot development includes many parameters to be analyzed, and trajectory planning and optimization are among them. The main objective of this study is to present a trajectory control and optimization algorithm for a cognitive architecture named auto-MERLIN. Optimization algorithms are developed for trajectory control. Results: The derived work empirically tests the solutions and provides execution details for creating the trajectory design. We develop the trajectory algorithm for the clockwise direction and another one for the clockwise and counterclockwise directions. Conclusions: Experimental results are drawn to support the proposed algorithm. Self-localization, self-driving, and right and left turn trajectories are drawn. All of the experimental results show that the designed TR works properly, with better accuracy and only a slight jitter in the orientation. The jitter is found due to the environmental factor caught by the sensors, which can be filtered easily. The results show that the proposed approach is less complex and provides better trajectory planning accuracy. © 2023 by the authors.

Socially distanced artistic careers: Professional social interactions in early, established, and late career stages during COVID-19

The impact of the first year of the COVID-19 pandemic on the arts sector resulted in acute, drastic drops in employment, revenue, and events. Career maintenance and persistence in the arts during this period involved substantially altered practices, particularly in terms of professional social interactions, which are known to be essential in artistic occupations. This research uses interview data from 66 U.S.-based arts graduates during the first year of the pandemic to establish how those in early, established, and late career stages experienced their professional social interactions. The findings show that the massive shift from in-person to almost solely online work and connectivity led to a drastic decrease in professional social interactions. Findings show that early career artists have the least social capital, established artists have the most, and late career artists begin to lose social capital unless they actively maintain it. Additionally, the "event-ized” nature of scheduling and attending work interactions digitally reduced feelings of community and collegiality. © 2023 Elsevier B.V.

CHARACTERIZING MOVEMENT PATTERNS OF NOMADIC PASTORALISTS AND THEIR EXPOSURE TO RIFT VALLEY FEVER IN KENYA

The role of animal movement in spreading infectious diseases is highly recognized by various legislations and institutions such as the World Organisation for Animal Health and the International Animal Health Code. The increased interactions at the nexus of human-animal-ecosystem interface have seen an unprecedented introduction and reintroduction of new zoonotic diseases with high socio-economic impacts such as the COVID-19 pandemic. Rift Valley fever (RVF) is a zoonotic disease that affects both humans and animals and is transmitted by Aedes mosquitoes or through contact with the body fluids of infected animals. This study seeks to characterize movement patterns of pastoralist and how this movement behaviour increases their susceptibility to RVF virus exposure. We levarage on a rapidly growing field of movement ecology to monitor five herds collared from 2013 – 2015 in an RVF endemic semi-arid region in Kenya. The herds were also sampled for RVF antibodies to assess their exposure to RVF virus during the rainy seasons. adehabitatLT package in R was used to analyze the trajectory data whereas the first passage time (FPT) analysis was used to measure the area utilized in grazing. Sedentary herds grazed within 15km radius while migrating herds presented restricted space use patterns during the dry seasons and transient movement during the start and end of the rainy season. Furthermore, RVF virus antibodies were generally low for sedentary herds whereas the migrating herds recorded high levels during their transition periods. This study can be used to identify RVF risk zones for timely and targeted management strategies.

Multi-view POI-level Cellular Trajectory Reconstruction for Digital Contact Tracing of Infectious Diseases

Digital contact tracing is an effective solution to prevent such a pandemic, but the low adoption rate of a required mobile app hinders its effectiveness. A large collection of cellular trajectories from mobile subscribers can be an out-of-the-box solution that is free from the low adoption issue, but has been overlooked due to its low spatial resolution. In this paper, to increase the resolution of this cellular trajectory, we present a new problem that estimates the user's visited places at the point-of-interest(POI) level, which we call POI-level cellular trajectory reconstruction. We propose a novel algorithm, Pincette, that accomplishes more accurate POI reconstruction by leveraging various external data such as road networks and POI contexts. Specifically, Pincette comprises multi-view feature extraction and GCN-LSTM-based POI estimation. In the multi-view feature extraction, Pincette extracts three complementary features from three views: efficiency, periodicity, and popularity. In the GCN-LSTM-based POI estimation, these three views are seamlessly integrated, where spatio-temporal periodic patterns are captured by graph convolutional networks (GCNs) and an LSTM. With extensive experiments on two real data collections of two cities, we show that Pincette outperforms four POI estimation baselines by up to 21.20%. We believe that our work sheds light on the use of cellular trajectories for digital contact tracing. We release the source code at https://github.com/kaist-dmlab/Pincette. © 2022 IEEE.

The New Evolutionary Trajectory of University Libraries: The Editor-in-Chief's View

The paper provides a brief overview of the events/issues that the authors of the 7(2022) issue of the UniLibNSD journal cover in their articles. The authors' many issues are highlighted through evidence of the nature of these challenges, as well as theoretical and concrete examples of how to address them. At a time of tectonic shifts in global geopolitics, climate change, digital development, the fight against the COVID 2019 pandemic, social transformations, librarians have witnessed russia's full-scale aggression against Ukraine and against world democracy, including the terrible destruction of Ukrainian libraries and archives by russian terrorists who continue their fierce offensive against the world's documentary heritage. That is why most of the authors challenge traditional concepts of librarianship, argue that libraries are not neutral, and call on the world's librarians to take active measures to prevent genocide, anti-racist and anti-oppressive practices for the benefit of both users and the profession itself. © T. O. Kolesnykova, 2022.