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Prediction of occurrence of a seizure would be of greater help to make necessary precaution for taking care of the patient. A Deep learning model, recurrent neural network (RNN), is designed for predicting the upcoming values in the EEG values. A deep data analysis is made to find the parameter that could best differentiate the normal values and seizure values. Next a recurrent neural network model is built for predicting the values earlier. Four different variants of recurrent neural networks are designed in terms of number of time stamps and the number of LSTM layers and the best model is identified. The best identified RNN model is used for predicting the values. The performance of the model is evaluated in terms of explained variance score and R2 score. The model founds to perform well number of elements in the test dataset is minimal and so this model can predict the seizure values only a few seconds earlier.

Abstract The Shared Meaning Model (SMM) is a grounded theory, derived in a previous study. This model demonstrates pathways for communication between nurse and primary care providers (PCPs) in the nursing home (NH), In this study we used the SMM for feasibility testing of a clinical decision support app (CDS app) using a descriptive, structured observational design. This study also provided a forum for initial testing of the SMM. The CDS app algorithm provided a common language to assess a resident with the goal of sharing this information with a PCP. The CDS app guided licensed vocational nurses (LVNs) (N=10) in assessing a standardized nursing home resident in a simulation setting experiencing symptoms of a potential urinary tract infection (UTI). Interviews with LVNs provided details of CDS app usability and concerns about using the CDS app with NH residents. Videos recorded LVNs interacting with the resident while using the CDS app on an iPad®. Time-stamps logged duration of the assessment. Bookmarked segments were used for discussion in LVN interviews. Videos were coded for eye contact, conversation, and touch between LVN and resident and documented personalized interactions. Findings indicated areas (lab values, drug names) for changes to language in the algorithm. In less than 12 minutes the CDS app enabled LVNs to collect information based on language used by PCPs to make decisions about the presence of a UTI. Relationships between initial constructs in the SMM were supported. This CDS app holds promise for building a common language to enhance interprofessional communication.

Information technology has become eminent in the development of modern cars. More than 50 Electronic Control Units (ECUs) realize vehicular functions in hardware and software, ranging from engine control and infotainment to future autonomous driving systems. Not only do the connections to the outside world pose new threats, but also the in-vehicle communication between ECUs, realized by bus systems such as Controller Area Network (CAN), needs to be protected against manipulation and replay of messages. Multiple countermeasures were presented in the past making use of Message Authentication Codes and time stamps and message counters, respectively, to provide message freshness, most prominently AUTOSAR’s Secure Onboard Communication (SecOC). In this paper, we focus on the latter ones. As one aspect of this paper, using an adequate formal model and proof, we will show that the currently considered solutions exhibit deficiencies that are hard if not impossible to overcome within the scope of the respective approaches. We further present a hardware-based approach that avoids these deficiencies and formally prove its freshness properties. In addition, we show its practicability by a hardware implementation. Finally, we evaluate our approach in comparison to counter-based solutions currently being used.

Recently, predicting multivariate time-series (MTS) has attracted much attention to obtain richer semantics with similar or better performances. In this paper, we propose a tri-partition alphabet-based state (tri-state) prediction method for symbolic MTSs. First, for each variable, the set of all symbols, i.e., alphabets, is divided into strong, medium, and weak using two user-specified thresholds. With the tri-partitioned alphabet, the tri-state takes the form of a matrix. One order contains the whole variables. The other is a feature vector that includes the most likely occurring strong, medium, and weak symbols. Second, a tri-partition strategy based on the deviation degree is proposed. We introduce the piecewise and symbolic aggregate approximation techniques to polymerize and discretize the original MTS. This way, the symbol is stronger and has a bigger deviation. Moreover, most popular numerical or symbolic similarity or distance metrics can be combined. Third, we propose an along–across similarity model to obtain the k-nearest matrix neighbors. This model considers the associations among the time stamps and variables simultaneously. Fourth, we design two post-filling strategies to obtain a completed tri-state. The experimental results from the four-domain datasets show that (1) the tri-state has greater recall but lower precision; (2) the two post-filling strategies can slightly improve the recall; and (3) the along–across similarity model composed by the Triangle and Jaccard metrics are first recommended for new datasets.

TikTok is one of the most popular apps. TikTok's endless stream of content, the lack time stamps or notifications of ever being “all caught up,” and concealing the phone's clock make it easy to lose track of time on TikTok. However, there is a lack of knowledge about how TikTok use may therefore interfere with our circadian rhythms, particularly our sleep hygiene. By focusing on pre-sleep cognitive arousal, this study aimed to close this knowledge gap by investigating the association between automatic TikTok use and daytime fatigue. We also investigated how individual preferences for sensation seeking and delayed gratification moderated this relationship. Within a sample of 1,050 TikTok/Douyin users in China, automatic TikTok use was associated with increased daytime fatigue that was mediated by higher levels of cognitive arousal before sleep. This relationship was aggravated by a preference for sensation seeking, and attenuated by a preference for delayed gratification. Above and beyond these early empirical insights, we also provide an early explanatory framework that is meant to systematize both existing and future knowledge about the use of TikTok.

A temporal digraph ${\cal G}$ is a triple $(G, \gamma, \lambda)$ where $G$ is a digraph, $\gamma$ is a function on $V(G)$ that tells us the time stamps when a vertex is active, and $\lambda$ is a function on $E(G)$ that tells for each $uv\in E(G)$ when $u$ and $v$ are linked. Given a static digraph $G$, and a subset $R\subseteq V(G)$, a spanning branching with root $R$ is a subdigraph of $G$ that has exactly one path from $R$ to each $v\in V(G)$. In this paper, we consider the temporal version of Edmonds' classical result about the problem of finding $k$ edge-disjoint spanning branchings respectively rooted in given $R_1,\cdots,R_k$. We introduce and investigate different definitions of spanning branchings, and of edge-disjointness in the context of temporal digraphs. A branching ${\cal B}$ is vertex-spanning if the root is able to reach each vertex $v$ of $G$ at some time where $v$ is active, while it is temporal-spanning if each $v$ can be reached from the root at every time where $v$ is active. On the other hand, two branchings ${\cal B}_1$ and ${\cal B}_2$ are edge-disjoint if they do not use the same edge of $G$, and are temporal-edge-disjoint if they can use the same edge of $G$ but at different times. This lead us to four definitions of disjoint spanning branchings and we prove that, unlike the static case, only one of these can be computed in polynomial time, namely the temporal-edge-disjoint temporal-spanning branchings problem, while the other versions are $\mathsf{NP}$-complete, even under very strict assumptions. 

We present a unique, comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus, or a taxi. The measurements are carried out by embedded sensor accelerometers in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they perform the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertically stored in an Excel Macro-enabled Workbook (xlsm) format that can be used to train an AI model in a smartphone which has the potential to collect people’s vibration data and decide what movement is being conducted. Moreover, with more data received, the database can be updated and used to train the model with a larger dataset. The prevalence of the smartphone opens the door to crowdsensing, which leads to the pattern of people taking public transport being understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transport, services and schedules can be planned perceptively.

This research compares and evaluates different approaches to approximate offense times of crimes. It contributes to and extends all previously proposed naïve and aoristic temporal approximation methods and one recent study [1] that showed that the addition of historical crimes with accurately known time stamps to temporal approximation methods can outperform all traditional approximation methods. It is paramount to work with crime data that possess precise temporal information to conduct reliable (spatiotemporal) analysis and modeling. This study contributes to and extends existing studies on temporal analysis. One novel and one relatively new temporal approximation methods are introduced that rely on weighting aoristic scores with historic offenses with exactly known offense times. It is hypothesized that these methods enhance the accuracy of the temporal approximation. In total, eight different methods are evaluated for apartment burglaries in Vienna, Austria, for yearly and seasonal differences. Results show that the one novel and one relatively new method applied in this research outperform all other existing approximation methods to estimate and predict offense times. These two methods are particularly useful for both researchers and practitioners, who often work with temporally imprecise crime data.

Abstract. Antarctic digital elevation models (DEMs) are essential for human fieldwork, ice topography monitoring and ice mass change estimation. In the past thirty decades, several Antarctic DEMs derived from satellite data have been published. However, these DEMs either have coarse spatial resolutions or vague time stamps, which limit their further scientific applications. In this study, the new-generation satellite laser altimeter Ice, Cloud, And Land Elevation Satellite-2 (ICESat-2) is used to generate a fine-scale and specific time-stamped Antarctic DEM for both the ice sheet and ice shelves. Approximately 4.69 × 109 ICESat-2 measurement points from November 2018 to November 2019 are used to estimate surface elevations at resolutions of 250 m, 500 m and 1 km based on a spatiotemporal fitting method, which results in a modal resolution of 250 m for this DEM. Approximately 74 % of Antarctica is observed, and the remaining observation gaps are interpolated using the ordinary kriging method. National Aeronautics and Space Administration Operation IceBridge (OIB) airborne data are used to evaluate the generated Antarctic DEM (hereafter called the ICESat-2 DEM) in individual Antarctic regions and surface types. Overall, a median bias of 0.11 m and a root-mean-square deviation of 8.27 m result from approximately 1.4 × 105 spatiotemporally matched grid cells. The accuracy and uncertainty of the ICESat-2 DEM vary in relation to the surface slope and roughness, and more reliable estimates are found in the flat ice sheet interior. The ICESat-2 DEM is superior to previous DEMs derived from satellite altimeters for both spatial resolution and elevation accuracy and comparable to those derived from stereo-photogrammetry and interferometry. The decimeter-scale accuracy and specific time stamp make the ICESat-2 DEM an essential addition to the existing Antarctic DEM groups, and it can be further used for other scientific applications.

Abstract Background Stroke patients with large vessel occlusion (LVO) require endovascular therapy (EVT) provided by comprehensive stroke centers (CSC). One strategy to achieve fast stroke symptom ‘onset to treatment’ times (OTT) is the preclinical selection of patients with severe stroke for direct transport to CSC. Another is the optimization of interhospital transfer workflow. Our aim was to investigate the dynamics of the OTT of ‘drip-and-ship’ patients as well as the current ‘door-in-door-out’ time (DIDO) and its determinants at representative regional German stroke units. Methods We determined the numbers of all EVT treatments, ‘drip-and-ship’ and ‘direct-to-center’ patients and their median OTT from the mandatory quality assurance registry of the federal state of Hesse, Germany (2012–2019). Additionally, we captured process time stamps from primary stroke centers (PSC) in a consecutive registry of patients referred for EVT in our regional stroke network over a 3 months period. Results Along with an increase of the EVT rate, the proportion of drip-and-ship patients grew steadily from 19.4% in 2012 to 31.3% in 2019. The time discrepancy for the median OTT between ‘drip-and-ship’ and ‘direct-to-center’ patients continuously declined from 173 to 74 min. The largest share of the DIDO (median 92, IQR 69–110) is spent with the organization of EVT and consecutive patient transfer. Conclusions ‘Drip-and-ship’ patients are an important and growing proportion of stroke patients undergoing EVT. The discrepancy in OTT for EVT between ‘drip-and-ship’ and ‘direct-to-center’ patients has been reduced considerably. Further optimization of the DIDO primarily aiming at the processes after the detection of LVO is urgently needed to improve stroke patient care.