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Here we intend to further the understanding of the planetary burden (and its dynamics) caused by the effect of the continued increase in carbon dioxide (CO2) emissions from fossil fuel burning and land use as well as by global warming from a new rheological (stress–strain) perspective. That is, we perceive the emission of anthropogenic CO2 into the atmosphere as a stressor and survey the condition of Earth in stress–strain units (stress in units of Pa, strain in units of 1) – allowing access to and insight into previously unknown characteristics reflecting Earth's rheological status. We use the idea of a Maxwell body consisting of elastic and damping (viscous) elements to reflect the overall behavior of the atmosphere–land and ocean system in response to the continued increase in CO2 emissions between 1850 and 2015. Thus, from the standpoint of a global observer, we see that the CO2 concentration in the atmosphere is increasing (rather quickly). Concomitantly, the atmosphere is warming and expanding, while some of the carbon is being locked away (rather slowly) in land and oceans, likewise under the influence of global warming. It is not known how reversible and how out of sync the latter process (uptake of carbon by sinks) is in relation to the former (expansion of the atmosphere). All we know is that the slower process remembers the influence of the faster one, which runs ahead. Important questions arise as to whether this global-scale memory – Earth's memory – can be identified and quantified, how it behaves dynamically, and, last but not least, how it interlinks with persistence by which we understand Earth's path dependency. We go beyond textbook knowledge by introducing three parameters that characterize the system: delay time, memory, and persistence. The three parameters depend, ceteris paribus, solely on the system's characteristic viscoelastic behavior and allow deeper and novel insights into that system. The parameters come with their own limits which govern the behavior of the atmosphere–land and ocean carbon system, independently from any external target values (such as temperature targets justified by means of global change research). We find that since 1850, the atmosphere–land and ocean system has been trapped progressively in terms of persistence (i.e., it will become progressively more difficult to relax the system), while its ability to build up memory has been reduced. The ability of a system to build up memory effectively can be understood as its ability to respond still within its natural regime or, if the build-up of memory is limited, as a measure for system failures globally in the future. Approximately 60 % of Earth's memory had already been exploited by humankind prior to 1959. Based on these stress–strain insights we expect that the atmosphere–land and ocean carbon system will be forced outside its natural regime well before 2050 if the current trend in emissions is not reversed immediately and sustainably.

The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF) aims at the provision and sound validation of well documented Climate Data Records (CDRs) in sustained and operational environments. In this study, a total column water vapour path (WVPA) climatology from CM SAF is presented and inter-compared to water vapour data records from various data sources. Based on homogenised brightness temperatures from the Special Sensor Microwave Imager (SSM/I), a climatology of WVPA has been generated within the Hamburg Ocean–Atmosphere Fluxes and Parameters from Satellite (HOAPS) framework. Within a research and operation transition activity the HOAPS data and operation capabilities have been successfully transferred to the CM SAF where the complete HOAPS data and processing schemes are hosted in an operational environment. An objective analysis for interpolation, namely kriging, has been applied to the swath-based WVPA retrievals from the HOAPS data set. The resulting climatology consists of daily and monthly mean fields of WVPA over the global ice-free ocean. The temporal coverage ranges from July 1987 to August 2006. After a comparison to the precursor product the CM SAF SSM/I-based climatology has been comprehensively compared to different types of meteorological analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF-ERA40, ERA INTERIM and operational analyses) and from the Japan Meteorological Agency (JMA–JRA). This inter-comparison shows an overall good agreement between the climatology and the analyses, with daily absolute biases generally smaller than 2 kg m−2. The absolute value of the bias to JRA and ERA INTERIM is typically smaller than 0.5 kg m−2. For the period 1991–2006, the root mean square error (RMSE) for both reanalyses is approximately 2 kg m−2. As SSM/I WVPA and radiances are assimilated into JMA and all ECMWF analyses and to assess consistency with existing WVPA climatologies, the SSM/I-based climatology is also compared to the time series of SSM/I and TMI (Tropical Rainfall Measuring Mission Microwave Imager) WVPA from Remote Sensing Systems (RSS), leading to results consistent with the reanalyses results. This evaluation study gives confidence in consistency, accurateness and stability of the total water vapour climatology produced.

The computational detection of R-peaks in an electrocardiogram (ECG) is particularly important for assessing vital signs in ambulatory settings. Algorithmic approaches are mainly open source and available for research purposes, primarily in the Python environment on stationary computers. This motivates the need for more support of mobile environments and their particular demands, such as context awareness and their limited computational resources.PeakSwift is a comprehensive Swift package designed for detecting R-peaks in single-lead ECG, tailored for the iOS environment. Its core functional features include on-device R-peak detection algorithms, quality assessment, and context-aware algorithm selection. PeakSwift is lightweight, extensible and modular. To evaluate its performance, a dedicated iOS application was developed, and a set of experiments was conducted using over 10,000 single-lead ECGs.The results demonstrated that PeakSwift achieves high reproducibility in detecting R-peaks compared to NeuroKit. In addition, its computational runtime was competitive.

Analyst sensemaking research typically focuses on individual or small groups conducting intelligence tasks. This has helped understand information retrieval tasks and how people communicate information. As a part of the grand challenge of the Summer Conference on Applied Data Science (SCADS) to build a system that can generate tailored daily reports (TLDR) for intelligence analysts, we conducted a qualitative interview study with analysts to increase understanding of information passing in the intelligence community. While our results are preliminary, we expect that this work will contribute to a better understanding of the information ecosystem of the intelligence community, how institutional dynamics affect information passing, and what implications this has for a TLDR system. This work describes our involvement in and work completed during SCADS. Although preliminary, we identify that information passing is both a formal and informal process and often follows professional networks due especially to the small population and specialization of work. We call attention to the need for future analysis of information ecosystems to better support tailored information retrieval features.

Federated learning (FL) enables the optimization of machine learning models on distributed clients without sharing local data. The integration of FL into a mobile environment is becoming more feasible due to increasing on-device processing capabilities. However, there is limited open-source support for the iOS platform.The article introduces a Swift-based client implementation of the user-friendly FL framework Flower. The objective is facilitating FL client processes based on a modular and easy-to-integrate software development kit.A benchmark test demonstrates consistent stability and performance using the software, further motivating its use for research.

There is a growing consensus in the global health community that the use of communication technologies will be an essential factor in ensuring universal health coverage of the world’s population. New technologies can only be used profitably if their accuracy is sufficient. Therefore, we explore the feasibility of using Apple’s ARKit technology to accurately measure the distance from the user’s eye to their smartphone screen. We developed an iOS application for measuring eyes-to-phone distances in various angles, using the built-in front-facing-camera and TrueDepth sensor. The actual position of the phone is precisely controlled and recorded, by fixing the head position and placing the phone in a robotic arm. Our results indicate that ARKit is capable of producing accurate measurements, with overall errors ranging between 0.88% and 9.07% from the actual distance, across various head positions. The accuracy of ARKit may be impacted by several factors such as head size, position, device model, and temperature. Our findings suggest that ARKit is a useful tool in the development of applications aimed at preventing eye damage caused by smartphone use.

Computer-vision-based frameworks enable markerless human motion capture on consumer-grade devices in real-time. They open up new possibilities for application, such as in the health and medical sector. So far, research on mobile solutions has been focused on 2-dimensional motion capture frameworks. 2D motion analysis is limited by the viewing angle of the positioned camera. New frameworks enable 3-dimensional human motion capture and can be supported through additional smartphone sensors such as LiDAR. 3D motion capture promises to overcome the limitations of 2D frameworks by considering all three movement planes independent of the camera angle. In this study, we performed a laboratory experiment with ten subjects, comparing the joint angles in eight different body-weight exercises tracked by Apple ARKit, a mobile 3D motion capture framework, against a gold-standard system for motion capture: the Vicon system. The 3D motion capture framework exposed a weighted Mean Absolute Error of 18.80° ± 12.12° (ranging from 3.75° ± 0.99° to 47.06° ± 5.11° per tracked joint angle and exercise) and a Mean Spearman Rank Correlation Coefficient of 0.76 for the whole data set. The data set shows a high variance of those two metrics between the observed angles and performed exercises. The observed accuracy is influenced by the visibility of the joints and the observed motion. While the 3D motion capture framework is a promising technology that could enable several use cases in the entertainment, health, and medical area, its limitations should be considered for each potential application area.

The aim of the present investigation was to assess if a mobile electroencephalography (EEG) setup can be used to track mental workload, which is an important aspect of learning performance and motivation and may thus represent a valuable source of information in the evaluation of cognitive training approaches. Twenty five healthy subjects performed a three-level N-back test using a fully mobile setup including tablet-based presentation of the task and EEG data collection with a self-mounted mobile EEG device at two assessment time points. A two-fold analysis approach was chosen including a standard analysis of variance and an artificial neural network to distinguish the levels of cognitive load. Our findings indicate that the setup is feasible for detecting changes in cognitive load, as reflected by alterations across lobes in different frequency bands. In particular, we observed a decrease of occipital alpha and an increase in frontal, parietal and occipital theta with increasing cognitive load. The most distinct levels of cognitive load could be discriminated by the integrated machine learning models with an accuracy of 86%.