Departure Events Research Articles

Eyeblink recognition improves fatigue prediction from single-channel forehead EEG in a realistic sustained attention task

Objective. A passive brain-computer interface recognizes its operator’s cognitive state without an explicitly performed control task. This technique is commonly used in conjunction with consumer-grade EEG devices for detecting the conditions of fatigue, attention, emotional arousal, or motion sickness. While it is easy to mount the sensors in the forehead area, which is not covered with hair, the recorded signals become greatly contaminated with eyeblink and movement artifacts, which makes it a challenge to acquire the data of suitable for analysis quality, particularly in few channel systems where a lack of spatial information limits the applicability of sophisticated signal cleaning algorithms. In this article, we demonstrate that by combining the features associated with electrocortical activities and eyeblink recognition analysis, it becomes feasible to design an accurate system for the inattention state prediction using just a single EEG sensor. Approach. Fifteen healthy 22–28 years old participants took part in the experiment that implemented a realistic sustained attention task of nighttime highway driving in a virtual environment. The EEG data were collected using a portable wireless Mindo-4 device, which constitutes an adjustable elastic strip with foam-based sensors, a data-acquisition module, an amplification and digitizing unit, and a Bluetooth module. Main results. The spectral analysis of the EEG samples that immediately preceded the lane departure events revealed alterations in the tonic power spectral density, which accompanied elongations in the drivers’ reaction times. The RMSE of the predicted reaction times, which are based on a combination of the brain-related and eyeblink features, is 0.034 ± 0.019 s, and the r2 is 0.885 ± 0.057 according to a within-session leave-one-trial-out cross-validation. Significance. The drowsiness prediction from a frontal single-channel setup can achieve a comparable performance with using an array of occipital EEG sensors. As a direct result of utilizing a dry sensor placed in the non-covered with hair head area, the proposed approach in this study is low-cost and user-friendly.

ISEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs.

Enforcing Bitrate-Stability for Adaptive Streaming Traffic in Cellular Networks

Video streaming over cellular network has become extremely popular in 4G and will be an integral part of future cellular networks. While most modern-day video clients continually adapt quality of video streams, they neither coordinate with network elements nor among each other. Consequently, a streaming client may quickly overload the cellular network, leading to poor Quality of Experience (QoE) for users in the network. Motivated by this problem, we present D-VIEWS — a scheduling paradigm that assures video bitrate stability of adaptive video streams while ensuring better system utilization. D-VIEWS only needs to be aware of the set of video bitrates and requires no changes to streaming clients and other network functions. Through simulations, we also study the performance of proportional fairness scheduler and D-VIEWS in the presence of user arrival and departure events.

Predictable shorebird departure patterns from a staging site can inform collision risks and mitigation of wind energy developments

High‐quality staging sites are critical for long‐distance migratory shorebirds to rest and refuel but are under threat from human development, including expansion of wind energy projects. However, predicting migration timing and movements in relation to weather conditions at staging sites can increase our understanding and mitigate effects of wind turbine collisions. Here we assessed northward migration timing and orientation in relation to environmental conditions at an inland staging area in Saskatchewan, Canada, with active and proposed wind energy developments. The area is known to host ~25% of North America's Sanderling Calidris alba population and 16 other Arctic‐breeding migrant shorebird species. We quantified arrival and departure time of day in relation to weather using data from 140 of 237 Sanderlings radiotagged locally and at a southern staging site in the Gulf of Mexico with the Motus Wildlife Tracking System (April–June, 2015–2017). Although Sanderling arrival times were not related to time of day or weather, departures were more likely at sunset in winds blowing towards the northwest at intermediate speeds (<22 km/h). Departure flights were also primarily oriented north‐northwest in the direction of a proposed wind energy development site at a mean ground speed of 21.4 m/s. Based on published climb rates and flight speed data, we estimated that shorebirds needed between 2 and 14 km setback distance to clear maximum turbine heights of 165 m. Given that departure events were predictable in time and space, adaptive mitigation may be useful for planning wind energy developments while reducing risk for staging Arctic‐breeding shorebirds.

Can your behaviour blow you away? Contextual and phenotypic precursors to passive aerial dispersal in phytophagous mites

Dispersal involves many behavioural interactions that if inefficient or ineffectual would lead to serious fitness consequences. Such costs would be particularly dear for passively dispersing organisms for which direction of movement is determined by external forces. In this study, we investigated whether environment and morphology influence dispersal behaviours of a passive, aerially dispersing organism, the eriophyoid wheat curl mite, Aceria tosichella. We used a wind tunnel to generate stable wind speeds and analysed mite behaviour, morphology and dispersal rate for each wind speed treatment. We found that dispersal events were phenotype and context dependent with an important behavioural component. Aerial dispersal increased significantly via chains formed by individuals and mites dispersing in chains were significantly more elongated than controls. This difference in shape interacted significantly with wind speed but diminished at high wind speeds. Contrary to expectations, we found that an upright position was not related to the likelihood of being blown away. The results indicate that departure events for this mite are influenced by the interaction of behaviour, morphology and environment. Our study represents a substantial step toward explaining wheat curl mite dispersal and its colonization potential, with implications for passively dispersing organisms in general.

Enhancing Non-intrusive Occupant Load Monitoring through Occupancy Matrix

It has been universally accepted that energy consumption in commercial buildings is highly related to occupant behaviors. Improving occupants’ energy-use behaviors is regarded as the most cost-effective approach to enhance overall energy saving in commercial built environments. However, effective behavior intervention pursuits rely on the availability of occupant-specific energy-use information, which is extremely expensive to capture with existing technologies. In this context, the author’s previous studies proposed the non-intrusive occupant load monitoring (NIOLM) approach that captures individual occupants’ energy-consuming information at their entry and departure events in an economically feasible manner. The NIOLM assigns energy-load variations (ev) of a building to individual occupants and relies on two variables: Time delay intervals and magnitudes of ev. This paper extends the existing NIOLM concept with the inclusion of a new variable, the occupancy matrix which manifests the information of present occupants at the moment of ev. An experiment has been conducted in an office space to validate the feasibility and accuracy of the proposed approach. Outcomes of this research could be a great help for studies on occupant energy-use behaviors intervention and simulation.

Lzts1 controls both neuronal delamination and outer radial glial-like cell generation during mammalian cerebral development

In the developing central nervous system, cell departure from the apical surface is the initial and fundamental step to form the 3D, organized architecture. Both delamination of differentiating cells and repositioning of progenitors to generate outer radial glial cells (oRGs) contribute to mammalian neocortical expansion; however, a comprehensive understanding of their mechanisms is lacking. Here, we demonstrate that Lzts1, a molecule associated with microtubule components, promotes both cell departure events. In neuronally committed cells, Lzts1 functions in apical delamination by altering apical junctional organization. In apical RGs (aRGs), Lzts1 expression is variable, depending on Hes1 expression levels. According to its differential levels, Lzts1 induces diverse RG behaviors: planar division, oblique divisions of aRGs that generate oRGs, and their mitotic somal translocation. Loss-of-function of lzts1 impairs all these cell departure processes. Thus, Lzts1 functions as a master modulator of cellular dynamics, contributing to increasing complexity of the cerebral architecture during evolution.

Web data for computing real-world noise from civil aviation

A large amount of data on civil air traffic is now available on the web, thanks to the recent development of flight tracker websites. This paper presents a novel methodology to exploit these data for computing real-world noise around airports. The proposed approach consists of data collection and pre-processing, flight path reconstruction, aircraft noise computation using a best-practice model (ECAC Doc.29), and rendering of ground track and noise contour maps in airport areas. Applications are shown for nine European airports, where the daily air traffic reconstructed from 10,752 collected flights compares well with official records from EUROCONTROL. Among these airports, London Heathrow, Amsterdam Schiphol and Vienna-Schwechat have been considered for the validation of the present methodology, and a good agreement is found between predicted ground noise levels and available historical data, with the largest deviations being detected in the portions of the airport areas most affected by departure events. The present work constitutes the first step to harnessing the potential of web data in aviation, unlocking unprecedented possibilities for assessing the environmental impact of civil aviation and providing policy-makers with a powerful tool for developing guidelines and regulations aimed at mitigating the detrimental effects of air traffic in densely inhabited regions.

A High-Computational Efficiency Human Detection and Flow Estimation Method Based on TOF Measurements.

State-of-the-art human detection methods focus on deep network architectures to achieve higher recognition performance, at the expense of huge computation. However, computational efficiency and real-time performance are also important evaluation indicators. This paper presents a fast real-time human detection and flow estimation method using depth images captured by a top-view TOF camera. The proposed algorithm mainly consists of head detection based on local pooling and searching, classification refinement based on human morphological features, and tracking assignment filter based on dynamic multi-dimensional feature. A depth image dataset record with more than 10k entries and departure events with detailed human location annotations is established. Taking full advantage of the distance information implied in the depth image, we achieve high-accuracy human detection and people counting with accuracy of 97.73% and significantly reduce the running time. Experiments demonstrate that our algorithm can run at 23.10 ms per frame on a CPU platform. In addition, the proposed robust approach is effective in complex situations such as fast walking, occlusion, crowded scenes, etc.

Performance of Kenyan Listed Companies Pre and Post Chief Executive Officer Dismissal, Retirement and Resignation Events

In the decade between years 2001 – 2011 Kenya experienced change of CEOs of listed firms through dismissal, resignation and retirement which created a need to establish whether CEO departure events had a significant influence on stock returns and profitability of the affected firms. In this research 9 CEO departure events 3 dismissal, 3 retirement and 3 resignation events were analyzed. Abnormal stock returns were computed by employing the market model and profitability of the affected listed firms was measured by employing return on assets (ROA). Paired samples t-test was employed to examine whether there was a significant difference in the abnormal returns and profitability (ROA) during pre and post CEO departure period. The findings indicated that there was no significant difference in abnormal stock returns and profitability in the pre and post CEO departure events. The findings implied that NSE investors were not excited by change of CEOs and that the NSE is semi strong form efficient.

Full-year tracking suggests endogenous control of migration timing in a long-distance migratory songbird

Following ongoing technological advances, an increasing amount of full-year tracking data on individual migratory movements is becoming available. This opens up the opportunity to study how migration develops within individuals in consecutive years and the extent to which the migratory program is constrained. Such knowledge is essential for understanding the degree of individual flexibility during the annual cycle, which may help identifying potential bottlenecks, where the range of individual decisions is restricted. In this study, we investigate repeatability in time of a long-distance migratory songbird, the red-backed shrike Lanius collurio, tracked across consecutive years (n = 7). Furthermore, we explore the population variability and dependencies between consecutive events of departure and arrival throughout the annual cycle in this species (n = 15). We find that individuals show high repeatability in timing of departure from their two main non-breeding areas in sub-Saharan Africa. In contrast, low repeatability is found in timing of arrivals to stationary sites throughout the annual cycle. Population variation in timing of departure and arrival was similar across all events, ranging from 30 to 41 days, and was highly dependent on timing of preceding events. We conclude that timing of departures is the key event potentially controlled by the individual innate migration program, while arrivals are more flexible, likely dependent on the environmental conditions experienced en route in red-backed shrikes. Still, apparent flexibility in the individual schedule may be hampered by overall constraints of the annual cycle. The annual migration schedule of migratory animals is controlled by a combination of endogenous and exogenous factors. Understanding the temporal dynamics within and between individuals across the annual cycle is important to assess to which extent the migratory schedule is constrained in time. By using full-annual cycle tracking data of individual red-backed shrikes tracked across consecutive years, we find that individuals are highly consistent in their decision to depart from their main non-breeding areas in sub-Saharan Africa, whereas arrivals are less consistent throughout the annual cycle. Overall, the migration schedule is highly constrained across the annual cycle, with each arrival and departure event being dependent on the previous event. Our results suggest that departure decision is underlying endogenous control and that little flexibility is available throughout this complex migration system.

Understanding the recurring patterns of occupants' energy-use behaviors at entry and departure events in office buildings

Office-building occupants' behaviors during their arrivals and departures foreseeably have a large impact on a building's energy consumption since many occupants in control of appliances will turn their devices on or off at these entry and departure events. Consequently, occupants would have various types of energy-use patterns that coincide with their entry and departure events and that repeat over time. Despite the value that knowledge of such patterns would have on better tracking energy-use behaviors, these patterns have not been well explored with empirical data in the literature. Therefore, this paper studies occupants' energy-use behaviors in office buildings to identify and investigate energy-use patterns at entry and departure events. In particular, this research evaluates (1) the delay intervals that manifest between the occupants' entry/departure events and the beginning/end of the occupants' energy-consuming behaviors, and (2) changes in electricity consumption caused by occupants at entry/departure events to identify recurring—and thereby predictable—energy-use patterns associated with individual occupants. In the pursuit of this objective, the energy-use behaviors of 12 occupants in two office buildings were tracked during a four-month period. Results from statistical analyses performed on the collected data reveal that an occupant in an office building typically follows a consistent, recurring delay-interval pattern. In addition, the results show each occupant also follows a recurring pattern of power changes at entry/departure events. By identifying recurring, occupant-specific energy-use behavior patterns, this study significantly contributes to the current body of research and can be used to support research efforts into energy-load disaggregation.

Stationary gaze entropy predicts lane departure events in sleep-deprived drivers

Performance decrement associated with sleep deprivation is a leading contributor to traffic accidents and fatalities. While current research has focused on eye blink parameters as physiological indicators of driver drowsiness, little is understood of how gaze behaviour alters as a result of sleep deprivation. In particular, the effect of sleep deprivation on gaze entropy has not been previously examined. In this randomised, repeated measures study, 9 (4 male, 5 female) healthy participants completed two driving sessions in a fully instrumented vehicle (1 after a night of sleep deprivation and 1 after normal sleep) on a closed track, during which eye movement activity and lane departure events were recorded. Following sleep deprivation, the rate of fixations reduced while blink rate and duration as well as saccade amplitude increased. In addition, stationary and transition entropy of gaze also increased following sleep deprivation as well as with amount of time driven. An increase in stationary gaze entropy in particular was associated with higher odds of a lane departure event occurrence. These results highlight how fatigue induced by sleep deprivation and time-on-task effects can impair drivers’ visual awareness through disruption of gaze distribution and scanning patterns.

Evaluation of Lane Departure Correction Systems Using a Regenerative Stochastic Driver Model

Evaluating the effectiveness and benefits of driver assistance systems is crucial for improving the system performance. In this paper, we propose a novel framework for testing and evaluating lane departure correction systems at a low cost by using lane departure events reproduced from naturalistic driving data. First, 529 096 lane departure events were extracted from the Safety Pilot Model Deployment database collected by the University of Michigan Transportation Research Institute. Second, a stochastic lane departure model consisting of eight random key variables was developed to reduce the dimension of the data description and improve the computational efficiency. With this purpose, we used a bounded Gaussian mixture model to describe drivers’ stochastic lane departure behaviors. Then, a lane departure correction system with an aim point controller was designed, and a batch of lane departure events was reproduced from the learned stochastic driver model. Finally, we assessed the developed evaluation approach by comparing lateral departure areas of vehicles between with and without correction controllers. The simulation results show that the proposed method can effectively evaluate lane departure correction systems.

Assessing drivers' response during automated driver support system failures with non-driving tasks

IntroductionWith the increase in automated driver support systems, drivers are shifting from operating their vehicles to supervising their automation. As a result, it is important to understand how drivers interact with these automated systems and evaluate their effect on driver responses to safety critical events. This study aimed to identify how drivers responded when experiencing a safety critical event in automated vehicles while also engaged in non-driving tasks. MethodIn total 48 participants were included in this driving simulator study with two levels of automated driving: (a) driving with no automation and (b) driving with adaptive cruise control (ACC) and lane keeping (LK) systems engaged; and also two levels of a non-driving task (a) watching a movie or (b) no non-driving task. In addition to driving performance measures, non-driving task performance and the mean glance duration for the non-driving task were compared between the two levels of automated driving. ResultsDrivers using the automated systems responded worse than those manually driving in terms of reaction time, lane departure duration, and maximum steering wheel angle to an induced lane departure event. These results also found that non-driving tasks further impaired driver responses to a safety critical event in the automated system condition. ConclusionIn the automated driving condition, driver responses to the safety critical events were slower, especially when engaged in a non-driving task. Practical applicationTraditional driver performance variables may not necessarily effectively and accurately evaluate driver responses to events when supervising autonomous vehicle systems. Thus, it is important to develop and use appropriate variables to quantify drivers' performance under these conditions.

On the Use of Monte-Carlo Simulation and Deep Fourier Neural Network in Lane Departure Warning

To make improvements on vision-based lane departure warning systems (LDWS), a lane departure prediction (LDP) method based on Monte-Carlo simulation and deep Fourier neural network (DFNN) is proposed. Firstly, a closed-loop driver-vehicle-road (DVR) system model is built up and the parameters of the system, consisting of vehicle states, positioning and road conditions, are initialized by random sampling. After simulating a large number of DVR systems with random parameters, the obtained results are used as samples to train a DFNN which predicts the forthcoming maximum lateral deviation and is optimized by employing deep learning method. Then, a LDP strategy is proposed by combining the DFNN with a driver activity index, which takes driver adaptation into consideration. The experimental evaluation shows that the proposed lane departure warning algorithm can predict the lane departure event in time and reduce the false-warning rate of existing methods in a significant way. More importantly, the proposed technique enhances the system's functions of over-speed warning on curved road and over-steer warning on low-adhesion road.

Driver Assistance System With a Dual Control Scheme: Effectiveness of Identifying Driver Drowsiness and Preventing Lane Departure Accidents

Driver drowsiness is a common cause of fatal traffic accidents. In this study, a driver assistance system with a dual control scheme is developed; it attempts to perform simultaneously the safety control of the vehicle and identification of the driver's state. The assistance system implements partial control in the event of lane departure and gives the driver the chance to voluntarily take the action needed. If the driver fails to implement the steering action needed within a limited time, the assistance system judges that “the driver's understanding of the given situation is incorrect” and executes the remaining control. We used a driving simulator equipped with the assistance system to investigate the effectiveness of identifying driver drowsiness and preventing lane departure accidents. Twenty students participated in three trials on a straight expressway, and they were required to implement only lateral control. We hypothesized that a participant cannot implement the action needed to maintain safety when he/she falls asleep, and that in such a case, the assistance system will implement the safety control repeatedly. The assistance system assisted the participants only when almost really needed, such as when their eyelids were closed. The results validated the hypothesis, showing that the assistance system implemented the safety control repeatedly when a participant fell asleep. In addition, the algorithms used by the assistance system to determine whether the driver can continue driving were evaluated through a leave-one-out cross-validation, and they were proven to be effective for identifying driver drowsiness.

Linking Building Energy-Load Variations with Occupants’ Energy-Use Behaviors in Commercial Buildings: Non-Intrusive Occupant Load Monitoring (NIOLM)

Studies indicate that occupancy-related energy-use behaviors have a significant influence on overall energy consumption in commercial buildings. In this context, understanding and improving occupants’ energy-consuming behaviors shows promise as a cost-effective approach to decreasing commercial buildings’ energy demands. Current behavior-modification pursuits rely on the data availability of occupant-specific energy consumption, but it is still quite challenging to track occupant-specific energy-consuming behaviors in commercial buildings. On the other hand, individual occupants have unique energy-consumption patterns at their entry and departure events and will typically follow such patterns consistently over time. Thus, analyzing occupants’ energy-use patterns at the time of their entry and departure events plays a critical role in understanding individual occupants’ energy-use behaviors. To this end, this paper aims to develop a non-intrusive occupant load monitoring (NIOLM) approach that profiles individual occupants’ energy-use behaviors at their entry and departure events. The NIOLM approach correlates occupancy-sensing data captured from existing Wi-Fi networks with aggregated building energy-monitoring data in order to disaggregate building energy loads to the level of individual occupants. Results from a 3-month long period of tracking individual occupants validate the feasibility of the NIOLM approach by comparing the framework's outcomes with the individual metering data captured from plug-load sensors. By utilizing existing devices and Wi-Fi network infrastructure, NIOLM provides a new opportunity for current industry and research efforts to track individual occupants’ energy-use behaviors at a minimal cost.

부자가족복지시설에 대한 문화기술지 연구: 부자가족아버지의 경험을 중심으로

The present study conducted ethnography of a welfare facility for single-father families. The participants were 13 single-fathers living in a welfare facility. To explore life within the welfare facility, the qualitative research method of ethnography was used with Spradley's developmental research sequence (DRS). The major results of this study were as follows. The first theme was 'finding a way to overcome the crisis: the facility is a lifeline.' For fathers, choosing to enter a facility meant enduring a social stigma, but the decision was made with the hope of achieving successful parenting and economic independence amidst pressing difficulties. The category of living in the facility was represented by the theme 'build the family: preparing for independence through living in a welfare facility.' The fathers achieved psychological recovery through the decrease in parenting stress, improvements in their economic situation brought by living in a facility, and used this time as the foundation to gain independence and build a stable life. The event of departure from the facility was seen as 'incomplete preparation for departure' by the single fathers. The fathers with infants or younger children admitted to being anxious about leaving the facility, which involved the burden of raising the children on their own. There are practical and policy-related implications based on the results of this study.

Out on their own: a test of adult-assisted dispersal in fledgling brood parasites reveals solitary departures from hosts

Brood-parasitic offspring sexually (mis)imprinting on the foster parents is considered one of the greatest constraints to the evolution of interspecific avian brood parasitism. While most nonparasitic juvenile birds learn the behaviours and mate choice preferences from their own parents, social parasites must avoid misimprinting on their host species' phenotype in order to accurately recognize conspecifics. One possible mechanism to assure accurate species recognition by juvenile parasites is to begin to associate with adult parasitic conspecifics, known as the ‘first contact’ scenario, whereby adult female parasites facilitate the dispersal of their offspring away from hosts, thus providing accurate referents for conspecific recognition. Using an automated radiotelemetry system, we determined the presence or absence (every 1–2 min during three breeding seasons; 516 315 search occasions) of radiotagged parasitic adult female brown-headed cowbirds, Molothrus ater , and compared their departures from a forest study site with those of genetic offspring or experimentally transplanted (nonrelated) juvenile cowbirds within the female's egg-laying range. Contrary to our predictions, we found no support for the facilitation of juvenile cowbird dispersal by adult female cowbirds. Juvenile cowbirds typically were not located within their mother's home range when departing the forest and, likewise, departure events for natal and experimentally transplanted juveniles (<2%) did not overlap temporally with the departure of the genetically assigned mother or with the departure of other local radiotagged female cowbirds. Surprisingly, we found that juvenile cowbirds primarily departed from the host's territory at sunset, when adult female cowbirds are infrequently present within the forest. Our results suggest that the solitary nocturnal roosting behaviour of juvenile cowbirds may facilitate independence from their hosts, thus minimizing the risk of misimprinting on heterospecific phenotypes. This strategy may also indirectly promote conspecific interactions, providing further evidence for the importance of independent spatial and social preferences of hosts and brood parasites in the evolution of avian brood parasitism. • Few studies have addressed the mechanism of species recognition in brood parasites. • We examined the movements of fledgling parasitic brown-headed cowbirds. • We tested whether adult female cowbirds guide juveniles from their hosts. • Fledgling cowbirds departed from the host solitarily and roosted alone. • Brood parasites may avoid (mis)imprinting on the host by spatial segregation.