Stopping Behavior Research Articles

The Impact of Multiple Births on Fertility: Stopping and Spacing in the United States During the Demographic Transition

Multiple births strain mothers’ and families’ resources in ways that should highlight preferences for family size, birth spacing, and parity-dependent stopping behavior. Couples with surviving twins reach their target family size sooner than other couples and should be more likely to practice family limitation. Twins are also a greater burden on the mother's time and health, which could lead to postponing the next birth, even among couples who want additional children. We examine these hypotheses by analyzing families with twins in the 1900 and 1910 U.S. Censuses. Using reconstructed birth histories for more than 7 million women in the IPUMS full-count 1900 and 1910 datasets and event-history methods (Kaplan–Meier curves, cure models), we find clear evidence of family limitation following a multiple birth. Couples who had twins or triplets were more likely to stop childbearing, and those who continued having children delayed their next birth. Responses to multiple births were larger in groups previously identified as leaders in the transition to smaller families, and roughly one third of couples stopped after one or two children. We find no evidence that some groups relied primarily on birth spacing to reduce family size while others relied primarily on stopping.

Younger children and mothers’ labour supply in rural India: Evidence from fertility stopping behaviour

This paper estimates the causal effect of having young children aged 0–5 years on mothers’ labour force participation in rural India. To address the potential endogeneity in the fertility decision, I exploit Indian families’ preference for having sons. I leverage exogenous variation in the gender of older children aged 6 + years as an instrumental variable for having younger children aged 0–5 years in the family. IV estimates show that the mothers’ participation is significantly reduced by 9.9% due to the presence of young children aged 0–5 years in the household, with the negative effect mostly driven by mothers belonging to the highest income quartile; mothers with high education; and mothers residing in nuclear families. The findings highlight the need for investment in high-skilled jobs and formal childcare facilities to encourage mothers’ labour supply. Using the testable implications for the generalizability of LATE discussed in Angrist (The Economic Journal, 114: C52 C83, 2004), I show that the estimated causal effect is homogenous across compliers, always takers, and never takers and thus, generalizable to the whole population of interest.

The effect of formal de‐policing on police traffic stop behavior and crime: Early evidence from LAPD's policy to restrict discretionary traffic stops

AbstractResearch SummaryOn March 1, 2022, correspondence from the Los Angeles Police Department (LAPD) headquarters stated that officers can no longer use minor infractions (e.g., equipment violations) as a pretextual reason to further investigate drivers for criminal behavior. If LAPD officers are to execute a discretionary stop, they must activate their body‐worn cameras and reasonably articulate to the civilian why they are being investigated. The intent is to reduce racial/ethnic disparities in stops and build trust of the police within the community. Critics of the policy argue that elevated crime rates will result due to the crime suppression effect of such stops. This study examines racial differences in stops before and after the policy change, as well as whether Part 1 violent and property crimes increased. Descriptive findings show that while the counts of stops, arrests, and contraband seizures during stops decreased, the percentage of non‐White civilians stopped decreased only minimally following the intervention. Results from using Bayesian synthetic control methods indicate an increase in both violent and property crimes post‐intervention compared with the synthetic counterfactual. The increase in violent crimes has a low probability of being different from the counterfactual, whereas the increase in property crimes has a high probability, suggesting that the intervention led to a real change in property crimes compared with what would have been expected under the counterfactual.Policy ImplicationsThis study provides preliminary evidence that emerging policies intended to restrict discretionary stops may not have the intended effect on racial disparities in police traffic stops. Instead, agencies passing similar policies may expect to see the potential unintended consequence of a spike in jurisdictional property crimes. We argue that such types of stops across a jurisdiction may have a general deterrent effect on more common and calculative crimes such as property crimes. To combat violent crimes, agencies should consider focusing discretionary stops in known hot spots. Further, our findings offer implications for research on the effects of de‐policing on crime. Police agencies should monitor potential unintended impacts of these policies if enacted and be prepared to deal with such consequences.

Son Targeting Fertility Behaviour in Albania

The collapse of communism led to highly skewed sex ratios in Albania, which had a long patriarchal tradition before the advent of communism. While the use of sex-selective abortions in the region is well-known, little is known about other forms taken by revealed son preference, such as differential stopping behaviour and birth spacing. Pooling data from the Demographic Health Surveys in 2008–2009 and 2017–2018, we find evidence of a higher proportion of boys being born at the last birth, indicating that parents practice differential stopping behaviour. Using a logit model, we also show that in sonless households parents shorten the birth intervals significantly, endangering mothers’ and children’s health. We conclude that differential stopping behaviour and short birth spacing are prevalent in all regions and across the socio-economic spectrum.

Recreation specialization: Resource selection functions as a predictive tool for protected area recreation management

Widespread and ongoing increases in park and protected area (PPA) visitation presents managers with the imminent challenge of preserving ecological conditions while also maintaining accessibility in the open spaces that are fundamental to the health and wellness needs of society. In the field of recreation resource management, recreation specialization, the selective channeling of interests and abilities into a specific recreational activity, has contributed to a more comprehensive understanding of recreation behavior, site preference, management perceptions and conservation support. Contributing to historical understandings of specialization can inform recreation planning on the diversity of uses occurring in urban-proximate PPAs. In this study, survey and GPS data collected in urban and peri-urban parks in the Nature Reserve of Orange County, CA, USA, were used to classify recreationists into three specialization types based on empirically derived dimensions of involvement, commitment and skill. For each survey participant, thirteen spatio-temporal metrics (STM) were calculated. A principle component analysis (PCA) reduced STMs into 3 factors representing expressions of spatial behavior and a one-way analysis of variance indicated unique patterns between specialization types and time spent recreating, elevation gained, speed traveled and stopping behavior. Additionally, GPS point data were analyzed with an analytical approach adopted from the field of wildlife movement ecology; a resource selection function (RSF). The RSF assisted in quantifying spatial distribution patterns specific to each specialization type across activity types at four park locations and demonstrated a landscape-based statistical analysis of probability of use in relation to change in elevation, distance to starting points and park amenities. Committed hikers and mountain bikers largely demonstrated longer trip durations, more elevation gain, less overall stopping, faster overall speeds and farther total distances, while e-bikers in this study demonstrated unintuitive specialization dynamics; casual specialization types travelling farther from starting points and dispersing more across trail systems. Further spatial results revealed spatial behavior to be inherently complex and influenced by numerous confounding factors (i.e. activity type/bike type, starting points, park topography and trail design). This novel spatial examination of recreation specialization according to activity type and park location, is useful in understanding recreation behavior and park use in a spatial context. This ability is helpful in a predictive managerial context when reviewed in correspondence with historical evidence identifying behaviors with increased potential for ecological impact.

Pulling back the veil of darkness: A proposed road map to disentangle racial disparities in traffic stops, a research note

AbstractThe veil of darkness (VOD) is a practical and rigorous methodology for examining racial disparities in police traffic stop behavior. Past research, however, has been littered with methodological inconsistencies inhibiting cross‐study comparison and decisions regarding policy. Accordingly, we clarify four aspects of its implementation: 1) coding daylight, our treatment condition; 2) constructing an intertwilight period; 3) accounting for seasonal differences in driving or patrol patterns; and 4) modeling VOD multivariable regression equations. We discuss the theoretical and practical implications of methodological decisions as they pertain to the method's functionality as a natural experiment. Furthermore, we propose a novel weighting procedure to account for seasonal driving population differences. We examined more than 50,000 traffic stops conducted by Michigan State Police during 2021 to demonstrate our suggested framework for future analyses.

Robust Loss Functions for Training Decision Trees with Noisy Labels

We consider training decision trees using noisily labeled data, focusing on loss functions that can lead to robust learning algorithms. Our contributions are threefold. First, we offer novel theoretical insights on the robustness of many existing loss functions in the context of decision tree learning. We show that some of the losses belong to a class of what we call conservative losses, and the conservative losses lead to an early stopping behavior during training and noise-tolerant predictions during testing. Second, we introduce a framework for constructing robust loss functions, called distribution losses. These losses apply percentile-based penalties based on an assumed margin distribution, and they naturally allow adapting to different noise rates via a robustness parameter. In particular, we introduce a new loss called the negative exponential loss, which leads to an efficient greedy impurity-reduction learning algorithm. Lastly, our experiments on multiple datasets and noise settings validate our theoretical insight and the effectiveness of our adaptive negative exponential loss.

Low blood concentration of alcohol enhances activity related to stopping failure in the right inferior frontal cortex.

This study investigated the effects of low doses of alcohol, which are acceptable for driving a car, on inhibitory control and neural processing using the stop-signal task (SST) in 17 healthy right-handed social drinkers. The study employed simultaneous functional magnetic resonance imaging and electromyography (EMG) recordings to assess behavioral and neural responses under conditions of low-dose alcohol (breath-alcohol concentration of 0.15mg/L) and placebo. The results demonstrated that even a small amount of alcohol consumption prolonged Go reaction times in the SST and modified stopping behavior, as evidenced by a decrease in the frequency and magnitude of partial response EMG that did not result in button pressing during successful inhibitory control. Furthermore, alcohol intake enhanced neural activity during failed inhibitory responses in the right inferior frontal cortex, suggesting its potential role in behavioral adaptation following stop-signal failure. These findings suggest that even low levels of alcohol consumption within legal driving limits can greatly impact both the cognitive performance and brain activity involved in inhibiting responses. This research provides important evidence on the neurobehavioral effects of low-dose alcohol consumption, with implications for understanding the biological basis of impaired motor control and decision-making and potentially informing legal guidelines on alcohol consumption.

On the (un)reliability of common behavioral and electrophysiological measures from the stop signal task: Measures of inhibition lack stability over time

Response inhibition, the intentional stopping of planned or initiated actions, is often considered a key facet of control, impulsivity, and self-regulation. The stop signal task is argued to be the purest inhibition task we have, and it is thus central to much work investigating the role of inhibition in areas like development and psychopathology. Most of this work quantifies stopping behavior by calculating the stop signal reaction time as a measure of individual stopping latency. Individual difference studies aiming to investigate why and how stopping latencies differ between people often do this under the assumption that the stop signal reaction time indexes a stable, dispositional trait. However, empirical support for this assumption is lacking, as common measures of inhibition and control tend to show low test-retest reliability and thus appear unstable over time. The reasons for this could be methodological, where low stability is driven by measurement noise, or substantive, where low stability is driven by a larger influence of state-like and situational factors. To investigate this, we characterized the split-half and test-retest reliability of a range of common behavioral and electrophysiological measures derived from the stop signal task. Across three independent studies, different measurement modalities, and a systematic review of the literature, we found a pattern of low temporal stability for inhibition measures and higher stability for measures of manifest behavior and non-inhibitory processing. This pattern could not be explained by measurement noise and low internal consistency. Consequently, response inhibition appears to have mostly state-like and situational determinants, and there is little support for the validity of conceptualizing common inhibition measures as reflecting stable traits.

Satellite tracking of American Woodcock reveals a gradient of migration strategies

Abstract Diversity in behavior is important for migratory birds in adapting to dynamic environmental and habitat conditions and responding to global change. Migratory behavior can be described by a variety of factors that comprise migration strategies. We characterized variation in migration strategies in American Woodcock (Scolopax minor), a migratory gamebird experiencing long-term population decline, using GPS data from ~300 individuals tracked throughout eastern North America. We classified woodcock migratory movements using a step-length threshold, and calculated characteristics of migration related to distance, path, and stopping events. We then used principal components analysis (PCA) to ordinate variation in migration characteristics along axes that explained different fundamental aspects of migration, and tested effects of body condition, age-sex class, and starting and ending location on PCA results. The PCA did not show evidence for clustering, suggesting a lack of discrete strategies among groups of individuals; rather, woodcock migration strategies existed along continuous gradients driven most heavily by metrics associated with migration distance and duration, departure timing, and stopping behavior. Body condition did not explain variation in migration strategy during the fall or spring, but during spring adult males and young females differed in some characteristics related to migration distance and duration. Starting and ending latitude and longitude, particularly the northernmost point of migration, explained up to 61% of the variation in any one axis of migration strategy. Our results reveal gradients in migration behavior of woodcock, and this variability should increase the resilience of woodcock to future anthropogenic landscape and climate change.

Behavioural economics theories in information-seeking behaviour research: A systematic review

This study aims to systematically reveal the application of behavioural economics theories in information-seeking behaviour research to promote the theoretical development of information-seeking behaviour research. A systematic review method was utilised to collect 92 relevant articles published from 1989 to 2022. The temporal development of these articles, research themes, and the theoretical contributions of behavioural economics theories to information-seeking behaviour research were organised and analysed. The findings are as follows: First, behavioural economics theories have been applied in information-seeking behaviour research for more than30 years and have entered an accelerated development stage. Second, behavioural economics theories are widely adaptable to information-seeking behaviour research. Early behavioural economics theories are applicable to 10 research topics such as information-seeking stopping behaviour and information evaluation, while contemporary behavioural economics theories are applicable to 16 research topics: information-seeking strategies and health information-seeking. Third, behavioural economics theories have produced four contributions to information-seeking behaviour research by revealing the mechanisms underlying information-seeking behaviour, exploring behavioural and cognitive strategies in information-seeking behaviour, interpreting the biases underlying information-seeking behaviour, and constructing theoretical models for information-seeking behaviour.

(Invited) Atomic Layer Processing of MoS2

Molybdenum disulfide (MoS2) is one of several transition metal dichalcogenides consisting of a layer of transition atoms sandwiched between layers of chalcogens. Interest in MoS2 has been driven by its 1.8 eV direct electronic band gap in monolayer form and its moderate electron mobility (10-100 cm2/Vs) even when only three atoms (~6.5 Å) thick. These properties offer potential for retaining or improving speed and efficiency in scaled electronic devices.Advancing MoS2 and other 2D materials into high volume manufacturing of semiconductor devices requires scalable deposition and etching processes. The process of atomic layer deposition (ALD) has been used to deposit 2D materials and can deposit films at lower temperatures for back-end-of-line (BEOL) compatibility. However, ALD relies on covalent surface reactions. When these reactions occur at high density during film nucleation, the resulting film is typically amorphous or nanocrystalline and requires thermal annealing to form a more well-ordered layered crystal structure. A key challenge is to establish a low temperature ALD process that achieves a layered crystal structure with low defectivity. In terms of material removal, MoS2 films have been etched by plasma-based atomic layer etching (ALE). ALE is analogous to ALD except that cyclic surface reactions promote volatility and lead to film removal rather than deposition. Together, atomic layer deposition and atomic layer etching constitute complementary facets of atomic layer processing and enable new pathways for nanomanufacturing.Here, we describe our recent progress in thermal ALD and thermal ALE of MoS2 films. For ALD of MoS2, we use cycles of MoF6 and H2S between 150-350 °C. Nucleation on metal oxide surfaces shows a strong temperature dependence, which is attributed in part due to the temperature dependence of surface hydroxyl groups. Density functional theory (DFT) calculations support the role of hydroxyls in promoting nucleation and reveal that MoF6 dissociates on the surface rather than participating in a ligand exchange reaction. The dissociation reaction leads to the formation of a metal fluoride interface between the oxide surface and the MoS2 film. Surface species following MoF6 include MoOF4 and MoO3, both of which convert to MoS2 during the H2S pulse, which releases H2O and HF byproducts. Films deposited at 200 °C and below are amorphous but convert to a layered structure upon annealing in H2S. Deposition at 250 °C yields films with a layered crystal structure without post-deposition annealing.Thermal ALE of MoS2 films deposited by ALD can be achieved using alternating exposures of MoF6 and H2O. It was found that MoS2 films are fluorinated by the MoF6 and then oxygenated by H2O. Volatile byproducts are MoF2O2, H2S, and HF, which are primarily released during the H2O half-cycle. Thermal ALE of MoS2 is temperature dependent and follows Arrhenius behavior. At 200 °C, the mass loss per cycle is 6 ng/cm2 (~0.5 Å/cycle) and reaches 271 ng/cm2 at 300 °C. Etch stop behavior was observed once the etched film approached the metal fluoride interface of the growth surface. Layered MoS2 deposited by ALD with post-deposition annealing exhibited layer-by-layer etching at 0.2 Å/cycle at 250 °C.The ALD results provide key insights into the nucleation and growth of MoS2 films using low temperature ALD. Further work is required to control basal plane orientation but achieving crystalline films well within BEOL thermal budgets is promising. The results from the ALE study provide insights into the etching reactions for MoS2. Combining the two processes offers greater control over MoS2 films. Using ALD followed by ALE and post-deposition annealing, we achieved few-layer MoS2 films. These combined thermal processes represent a pathway for integration of MoS2 films into device manufacturing.

Proactive cues facilitate faster action reprogramming, but not stopping, in a response-selective stop signal task

The ability to stop simple ongoing actions has been extensively studied using the stop signal task, but less is known about inhibition in more complex scenarios. Here we used a task requiring bimanual responses to go stimuli, but selective inhibition of only one of those responses following a stop signal. We assessed how proactive cues affect the nature of both the responding and stopping processes, and the well-documented stopping delay (interference effect) in the continuing action following successful stopping. In this task, estimates of the speed of inhibition based on a simple-stopping model are inappropriate, and have produced inconsistent findings about the effects of proactive control on motor inhibition. We instead used a multi-modal approach, based on improved methods of detecting and interpreting partial electromyographical responses and the recently proposed SIS (simultaneously inhibit and start) model of selective stopping behaviour. Our results provide clear and converging evidence that proactive cues reduce the stopping delay effect by slowing bimanual responses and speeding unimanual responses, with a negligible effect on the speed of the stopping process.

Strategy and safety at stop intersections in older adults with mild cognitive impairment and visual decline

This study assessed the impact of age-related cognitive and visual declines on stop-controlled intersection stopping and scanning behaviors across varying roadway, traffic, and environmental challenges.Real-world driver data, collected from drivers’ personal vehicles using in-vehicle sensor systems, was analyzed in 68 older adults (65–90 years old) with and without mild cognitive impairment (MCI) and with a range of age-related visual declines. Driver behavior, environmental characteristics, and traffic characteristic were examined across 2,596 approaches at 173 stop-controlled intersections. A mixed-effects logistic regression modeled stopping behavior as a binary response (full stop or rolling/no-stop).Overall, drivers who scanned more on intersection approaches (OR = 0.77) or had more visual decline (OR = 2.28) were more likely to make full stops at a stop-controlled approach. Drivers with a contrast sensitivity logMAR score > 0.8 showed the greatest probability of making a full stop compared across all drivers. Drivers without MCI were ∼ 5 times more likely to come to a full stop when they scanned more (23 % versus 5 % when they scanned less) compared to drivers with MCI, who were only twice as likely to stop (14 % versus 6 % when they scanned less). Drivers were more likely to fully stop on two-lane roadways (1.5 %), during night (2.0 %), and at intersections with opposing vehicles (10.4 %).Findings illuminate how driver strategies interact with underlying impairment. While drivers with visual decline adopt strategies that may improve safety, when drivers with MCI adopt strategies it did not result in the same degree of improvement in stopping which may result in greater risk.

Modeling the Contact Force in Constrained Human–Robot Collisions

Collaborative robots (cobots) become more and more important in industrial manufacturing as flexible companions, working side by side with humans without safety fences. A key challenge of such workplaces is to guarantee the safety of the human co-workers. The safeguarding Power and Force Limiting, as specified by ISO 10218-2 and ISO/TS 15066, has the objective to protect humans against robot collisions by preventing the robot from exceeding biomechanical limits. Unintended contact such as collisions can occur under unconstrained spatial conditions (a human body part can move freely) or constrained spatial conditions (a human body part is pinched). In particular, collisions under constrained conditions involve a high risk of injury and thus require the robot to stop immediately after detecting the collision. The robot’s speed has a significant influence on its stopping behavior, though, and thus on the maximum collision forces that the robot can exert on the human body. Consequently, a safe velocity is required that avoids the robot from exerting forces and pressures beyond the biomechanical limits. Today, such velocities can only be ascertained in costly robot experiments. In this article, we describe a model that enables us to determine the contact forces of a cobot as they occur in constrained collisions. Through simulations, it becomes possible to iteratively determine the maximum safe velocity for a specific contact hazard that occurs under constrained spatial conditions. Experimental tests with different cobots confirm the results of our model, albeit not for all robots. Despite the mixed test results, we strongly believe that our model can significantly improve the reliability of assumptions made today during the planning of cobots.

Discovering stop and parking behaviors of last mile delivery vehicles for urban areas based on not well conditioned GPS traces, expert knowledge and machine learning

Nowadays urban traffic is one of the most serious challenges for local authorities around the world. This challenge implies issues regarding health, energy, safety, environment and quality of life. Achieving a fair traffic outcome is a key priority for city traffic managers while maintaining city’s attractiveness for citizens and travelers and those carrying out commercial activities. As a part of this challenge, delivery and similar companies are relevant stakeholders in optimizing their routes, deliveries, and operational services. It is in this respect that urban traffic and its regulations play a key role.The conditions in which urban traffic takes place thus have to be known and one way to deal with this is based on available and accessible data. In the IoT age, there could be huge amounts of available data generated by countless sensors and systems involved in our lives, cities, infrastructures, etc.... Taking advantage of such data, when the accessibility and quality is good enough, can help us to achieve the desired goals concerning the urban traffic and its consequences. However, in practice, the availability and access to such data is currently a very serious challenge.Following on with this, data generated from the ever-increasing number of sensors on board vehicles could be very useful, not only for checking the vehicle condition, but also to gain a better of the “real-time” traffic situation or to discover traffic behaviors/patterns from the said data. In this work, a real case based study has been carried out gathering basic real GPS information regarding delivery vehicles in a city environment and OpenData to “discover” where, when and how long time delivery vehicles use the regulated parking zones for loading/unloading in the city center. Based on Expert Rules, a stop detection criteria is defined and formulated to be applied to real cases in urban areas, focusing on city centers and Machine Learning techniques to discover stop and park behaviors on last mile deliveries in a real urban area.All this is used to plan traffic strategies and facilities which can permit better and more fluent services. On the other hand, the results provide invaluable knowledge support for the expert knowledge of mobility managers, while also supplying new “findings” about the daily challenges, showing that machine learning techniques and other linked technologies are powerful tools for this challenge.

Pedestrian Safety: Drivers’ Stopping Behavior at Crosswalks

The safety of a pedestrian crossing may depend on infrastructure, vehicular and pedestrian traffic characteristics. This academic research typically portrays the safety challenges knowingly caused by vehicles on crosswalks in the city of Kigali. Through carefully observing the stopping of local drivers in pedestrian crossing events, the study aims to objectively evaluate drivers’ questionable behavior against traffic flow parameters. Ten collection sites were ultimately selected purposefully and randomly to suit observations for data recording. A grand total of 10,259 crossing events were properly recorded within 280 h. Statistical analyses, practical tests and binary logistic regression models were promptly used to adequately evaluate the specific behaviors. Woefully, 82.4% of drivers violate crosswalks, endangering crossing pedestrians. Motorcyclists typically exhibit the most aggressive behavior. Car drivers are relatively less aggressive, whereby 60% managed to brake in the events. Local buses and authorized bicycles enthusiastically shared a negligible collective percentage of 2%, being aggressive and not stopping. In general, cars are 10.389 times more likely to voluntarily and justly stop compared to bicycles. Maintaining more vehicles in a row is safer for a pedestrian to cross, as for each unit increase on the vehicle density scale, there were 1.956 more chances that every driver would stop. In brief, 13% to 21% of traffic variables positively predict the considerable variance in the stopping behavior model.

Stopping a Continuous Movement: A Novel Approach to Investigating Inhibitory Control.

Flexible, adaptive behavior is critically dependent on inhibitory control. For example, if you suddenly notice you are about to step on a tack and would prefer not to, the ability to halt your ongoing movement is critical. However, this behavior is usually not probed by current inhibitory control tasks, which often focus instead on inhibiting a movement before its initiation. To address limitations in existing approaches for studying termination of an ongoing movement, we developed a novel stop task with which we can directly observe and compare prepared and reactive termination of a continuous movement. Here, we present and evaluate our novel continuous movement stop task (CMST) and compare task performance to performance on the traditional stop signal task. Our data reveal that the CMST effectively dissociates planned and unplanned stopping behaviors. We found that participants initiated and completed stopping significantly earlier on planned compared with unplanned stop trials and that the variability for each measure was greater for planned compared with unplanned stop trials. In addition, we found that the time at which participants initiated the stopping process was more variable than the time it took participants to complete the stopping process. We also found that participants slowed before stopping significantly more on planned than unplanned stop trials. Finally, our data suggest that preparatory mechanisms may be similar between the two tasks, but the tasks were not related by any other measure. The unambiguous quantification of prepared and reactive stopping behavior provided by the CMST will help support future investigation of different kinds of stopping behavior.

A comparative study on pedestrian flow through bottlenecks between flood evacuation and land evacuation

In a flood, people may be forced to evacuate in water until reaching a secure shelter. Understanding the characteristics of pedestrian flow in water is therefore of utmost importance. Some researchers studied individual walking speed in water, whereas it is still rare to study pedestrian and evacuation dynamics, e.g., characteristics of pedestrian flow through a bottleneck. In this paper, evacuation experiments through bottlenecks are conducted in 0.60-m-deep water, with 97 volunteers with an average age of 21.7 years participating. The results are compared with those on land. It is shown that the flow rate in water is lower than that on land, with a difference varying with bottleneck width b as ΔJ=0.60b−0.04ped/s. The average specific flow rate in water is 0.43ped/(m·s) lower than that on land. Based on the relation of time headway–width, density–width, and speed–width, threshold widths for the flow transition from “interrupted” to “flowing” stages are 1.3 m in water and 1.0 m on land respectively. Compared with that on land, during “interrupted” stage in water, stop behavior is more frequent, the speed near the exit is 0.22 m/s lower, and the speed-up point is closer to the exit. During “flowing” stage, continuity of the pedestrian flow at exit is inferior in water compared to that on land, a 0.2–0.3 m wider exit is required in water to achieve similar continuity on land. These findings may provide valuable insights into emergency management during flood evacuation.

The Effect of Symptom-Provocation on Inhibitory Control in Obsessive-Compulsive Disorder Patients Is Contingent upon Chronotype and Time of Day.

Studies have shown that alertness can affect inhibitory control, the mechanism responsible for stopping behaviors, thoughts, or emotions. Inhibitory control is particularly important for helping individuals with Obsessive-Compulsive Disorder (OCD) resisting their symptoms. Chronotype is the mechanism governing an individual's fluctuation of alertness throughout the day. Previous studies have shown that individuals with a 'morning' chronotype have worse OCD symptoms in the evening and vice versa. We administered a novel 'symptom-provocation stop signal task' (SP-SST), in which individually tailored OCD triggers were presented and inhibitory control was measured. Twenty-five treatment-seeking OCD patients completed the SP-SST three times per day for seven consecutive days. Stop signal reaction time (SSRT), which measures inhibitory control, was calculated separately for symptom-provocation trials and for neutral trials. Results yielded that: (a) stopping was significantly harder in the symptom-provocation compared to neutral trials, and (b) the chronotype by time-of-day interaction predicts inhibition for both symptom-provocation and neutral trials, indicating better inhibition in the optimal time of day. Furthermore, we concluded that individually tailored OCD triggers have a detrimental effect on inhibitory control. Most importantly, higher alertness levels, which can be predicted by the interaction of chronotype and time of day, affect inhibitory control, both in general and for OCD triggers specifically.