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A new research domain known as the Quantified Self has recently emerged and is described as gaining self-knowledge through using wearable technology to acquire information on self-monitoring activities and physical health related problems. However, very little is known about the impact of time window models on discovering self-quantified patterns that can yield new self-knowledge insights. This paper aims to discover the self-quantified patterns using multi-time window models.

This paper proposes a multi-time window analytical workflow developed to support the streaming k-means clustering algorithm, based on an online/offline approach that combines both sliding and damped time window models. An intervention experiment with 15 participants is used to gather Fitbit data logs and implement the proposed analytical workflow.

The clustering results reveal the impact of a time window model has on exploring the evolution of micro-clusters and the labelling of macro-clusters to accurately explain regular and irregular individual physical behaviour.

The preliminary results demonstrate the impact they have on finding meaningful patterns.

Present a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts.

Using the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lightning prediction.

The techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction.

The research is limited to the data collected in support of weather prediction work through the 45th Weather Squadron of the United States Air Force.

These methods are important due to the increasing reliance on sensor systems. These systems often provide incomplete and chaotic data, which must be used despite collection limitations. This work establishes a viable data imputation methodology.

Improved lightning prediction, as with any improved prediction methods for natural weather events, can save lives and resources due to timely, cautious behaviors as a result of the predictions.

Based on the authors’ knowledge, this is a novel application of these imputation methods and the forecasting methods.

Authorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.

A decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.

The comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.

The efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

The purpose of this paper is to investigate US noncombatant evacuation operations (NEO) in South Korea and devise planning and management procedures that improve the efficiency of those missions.

It formulates a time-staged network model of the South Korean noncombatant evacuation system as a mixed integer linear program to determine an optimal flow configuration that minimizes the time required to complete an evacuation. This solution considers the capacity and resource constraints of multiple transportation modes and effectively allocates the limited assets across a time-staged network to create a feasible evacuation plan. That solution is post-processed and a vehicle routing procedure then produces a high resolution schedule for each individual asset throughout the entire duration of the NEO.

This work makes a clear improvement in the decision-making and resource allocation methodology currently used in a NEO on the Korea peninsula. It immediately provides previously unidentifiable information regarding the scope and requirements of a particular evacuation scenario and then produces an executable schedule for assets to facilitate mission accomplishment.

The significance of this work is not relegated only to evacuation operations on the Korean peninsula; there are numerous other NEO and natural disaster related scenarios that can benefit from this approach.

The expected benefits of newly developed transportation infrastructures are the saving of travel time and further promoted transport economics. There is a need for a methodology of travel time estimation with acceptable robustness and practicability. Macroscopic fundamental diagram (MFD) represents the overall traffic performance at a network level by linking average flow, speed and density. MFD can be used to estimate network state and to describe various traffic management strategies. This study aims to describe the effect of new infrastructure development on the network performance using the MFD framework.

The scenarios of Islamabad Road network before and after the infrastructure construction were simulated, in which the floating car data set (FCD) for multiple modes was extracted. MFD has been formed for the whole region and partitioned region, which was divided on the basis of infrastructural changes. Moreover, this study has been extended to calculate travel time for multiple modes using the MFD results and the Bureau of Public Roads (BPR) function at a neighborhood level.

MFD results for the whole network showed that the speed of traffic improves after the construction of new infrastructure. The travel time estimates using MFD results were dependent on the speed estimates, whereas the estimates obtained using the BPR function were found to be dependent on the traffic volume variation during different intervals of the day. By using the FCD for multiple modes, travel time estimates for multiple modes were obtained. The BPR function method was found valid for estimating travel time of traffic stream only.

This paper innovatively investigates the change in network performance for pre-construction and post-construction scenarios using the MFD framework. In practice, the approach presented can be used by transportation agencies to evaluate the effect of different traffic management strategies and infrastructural changes.

This article explores, identifies and conceptualises everyday audiobook reading practices amongst young adults.

Semi-structured interviews were conducted with ten Swedish audiobook users aged 18–19. The material was analysed using qualitative content analysis and focused on their audiobook use during an average weekday, as this was the time that they listened the most. The theoretical framework consists of theories on practice, time and everyday routine.

Five timespaces emerged when audiobook practices were most prevalent: morning routines, commuting routines, school routines, after school routines and bedtime routines. Within these timespaces, several practices could be identified and conceptualised. Three mobile practices were commute listening, exercise listening and chore listening while more stationary practices were homework listening, schoolwork listening and leisure listening. An unexpected finding was how audiobooks routinely were used to aid respondents’ wellbeing. This wellbeing listening was used to alleviate stress, loneliness and help listeners relax or fall asleep. Furthermore, respondents switch between Music, Audiobooks and Podcasts, which is conceptualised as MAP-switching.

There is a scarcity of research on audiobook use, and this paper contributes with new knowledge on audiobook reading practices, how audiobooks fit into everyday routine and provides concepts to aid further research on audiobook practices.

The purpose of this paper is to develop a proof-of-concept (POC) Forward Collision Warning (FWC) system for the motorcyclist, which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle (motorcycle).

This comes in three approaches. First, time-to-collision value is to be calculated based on low-cost camera video input. Second, the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate. Third, the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor.

This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above. First, to predict time-to-collision, nested Kalman filter and vehicle detection is used to convert image pixel matrix to relative distance, velocity and time-to-collision data. Next, for trajectory prediction of detected vehicles, a few algorithms were compared, and it was found that long short-term memory performs the best on the data set. The last finding is that to determine the leaning direction of the ego vehicle, it is better to use lean angle measurement compared to riding pattern classification.

The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle (motorcycle).

Research shows that parental employment and education status affect the amount of parental childcare time, which is a fundamental determinant of children's outcomes. In this paper, the authors study whether being overeducated – working in a job that requires less education than the level of education acquired – is related to the time parents devote to their children.

The authors set two main hypotheses. First, overeducation might lead to more childcare time if being overeducated is the result of the individual prioritizing family over career. Second, overeducation might lead to less childcare time if overeducation is the result of lower ability. The authors estimate time use equations using the American Time Use Survey (ATUS) from 2004 to 2019.

The authors find that overeducated parents devote less time to childcare than matched parents, especially in the weekend sample. The authors’ results suggest that overeducation is not a deliberate choice prioritizing family over career.

To the best of the authors’ knowledge, this is the first study on the implications of being overeducated on childcare.

The purpose of this study is to categorize perfectionism and determine how perfectionism impacts the occupations and perceived health of students in a Bachelor of Science in Occupational Science program.

A descriptive study with a survey component was conducted. Participants were categorized as perfectionists or non-perfectionists using the Almost Perfect Scale-Revised (APS-R). Time logs were collected to compare categories of time-use between groups over a one-week period. An online survey was conducted with a sub-sample of the perfectionists.

More students were categorized as perfectionists (N = 41) than non-perfectionists (N = 3). Both groups spent similar amounts of time engaged in productive, pleasurable and restorative occupations. Some perfectionists reported that perfectionism supported health, but others reported negative impacts on well-being.

This study included a small sample size limited to one Occupational Science program in the USA.

Results demonstrated positive and negative health impacts because of perfectionism. The majority of participants were identified as perfectionists; rigorous academic programs may attract students with perfectionistic qualities. Findings are relevant for Occupational Therapy, as these students will become future occupational therapists after completing a Master’s program in Occupational Therapy and may be susceptible to negative outcomes associated with perfectionism such as workaholism and poor health.

The need to change budgeting has been frequently debated. Drawing on the literature on management accounting and budgeting change, this study aims to explore changes in budgeting and whether experienced success of budgeting varied with time and budget type. Changes in the use of the following budget types were investigated: fixed, revised, rolling, flexible and hybrid budgets.

This study uses a mixed research methodology. Survey data was collected from the same business units of large Finnish manufacturing firms in 2004 (Time 1) and 2016/2017 (Time 2) (N = 28). In addition, some of the respondents of the latter survey were interviewed in 2023 (Time 3).

Almost all business units were found to have remained loyal to budgeting. However, changes in budget types were not uncommon and varied considerably. Overall, the use of fixed budgets continued strongly, the use of revised and hybrid budgets declined, and the use of rolling budgets increased over time. Moreover, the joint use of budgets declined. The perceived success of budgetary processes was, initially, weakened by the use of fixed budgets and, later, by the use of revised budgets. The interview data further illustrates some of the patterns of, and reasons behind, the changes.

Longitudinal analysis of change in the same business units was useful in revealing the patterns of change in budgeting and on relationships between the variables analysed over time. Further research could be carried out using more extensive case studies in companies or sector-focused surveys longitudinally.