While environmental life cycle assessment is an established method for predicting environmental impacts over the lifetime of a structure or building, and is supported by ISO-norms, it overlooks social impacts such as structural safety. The more comprehensive life cycle sustainability
assessment, which also includes economic and societal sustainability, is not as mature. There is especially a lack of quantitative indicators for the societal impacts of a structure, which form part of social life cycle assessment.

This paper investigates the use of an existing societal indicator, the Life Quality Index, which has not been used in social life cycle assessment before. It has, however, been used previously in structural engineering applications to establish societally acceptable and economically optimal failure probabilities of structures. In this paper, this use is compared to the most recent guidelines on social life cycle assessment by the United Nations Environmental Programme.

This paper proposes that the current use of the life quality index can be part of the social impact assessment phase of social life cycle assessment. It then forms part of a social mechanism within an impact pathway approach, one of the two approaches towards social impact assessment proposed by the guidelines. This is demonstrated using an example based on the design of a simple structure, following the four phases of a life cycle assessment. The demonstrated approach is able to combine societal and economic considerations, making it a promising candidate for future applications in life cycle sustainability assessment of structures.

In recent years, the relationship between academia and the fossil fuel industry has become a focal point of intense debate. This concern arises from the fear that corporate funding might skew research activities. A significant development in this area is the adoption of policies by a Dutch university, and discussions in several others, prohibiting research funded by the fossil fuel industry. These policies aim to safeguard academic freedom and integrity. Despite this, there has been little discussion on the myriad challenges, implications, and possible unintended consequences, particularly in the realm of safety-and-security research. As such, this manuscript delves into the complex transition towards a fossil-fuel-free society, examining it through the lenses of safety science and sociotechnical systems. It emphasizes the vital importance of collective responsibility in ensuring systemic safety and security as we navigate towards achieving the sustainable development goals. This journey requires a delicate balance between the objectives of safety and sustainability, along with a deep understanding of the security implications of decreasing our dependence on the fossil fuel industry. The strategy of distancing academic research from fossil fuel industries, commonly seen as a positive step, also demands a nuanced consideration of its broader impacts, including the setting of precedents for addressing other existential and systemic risks. Instead, we argue for the establishment of robust governance structures rooted in restorative justice principles. Such frameworks can facilitate productive dialogue with underrepresented groups, motivate the fossil fuel industry towards sustainable practices, and safeguard the integrity of scholarly research. This approach not only addresses immediate concerns related to fossil fuels but also lays the groundwork for a more inclusive and equitable model of climate risk research, essential for tackling the multifaceted challenges of our era.

As automated vehicles (AVs) become increasingly popular, the question arises as to how cyclists will interact with such vehicles. This study investigated (1) whether cyclists spontaneously notice if a vehicle is driverless, (2) how well they perform a driver-detection task when explicitly instructed, and (3) how they carry out these tasks. Using a Wizard-of-Oz method, 37 participants cycled a designated route and encountered an AV multiple times in two experimental sessions. In Session 1, participants cycled the route uninstructed, while in Session 2, they were instructed to verbally report whether they detected the presence or absence of a driver. Additionally, we recorded participants’ gaze behaviour with eye-tracking and their responses in post-session interviews. The interviews revealed that 30% of the cyclists spontaneously mentioned the absence of a driver (Session 1), and when instructed (Session 2), they detected the absence and presence of the driver with 93% accuracy. The eye-tracking data showed that cyclists looked more frequently and for longer at the vehicle in Session 2 compared to Session 1. Additionally, participants exhibited intermittent sampling of the vehicle, and they looked at the area in front of the vehicle when it was far away and towards the windshield region when it was closer. The post-session interviews also indicated that participants were curious, but felt safe, and reported a need to receive information about the AV's driving state. In conclusion, cyclists can detect the absence of a driver in the AV, and this detection may influence their perception of safety. Further research is needed to explore these findings in real-world traffic conditions.

The i-DREAMS project focuses on establishing a framework known as the ‘Safety Tolerance Zone (STZ)’ to ensure drivers operate within safe boundaries. This study compares Long-Short-Term-Memory Networks and shallow Neural Networks to assess participants’ safety levels during i-DREAMS on-road trials. Thirty German drivers’ trips and Forty-Three Belgian drivers were analyzed using these methods, revealing factors contributing to risky behavior. Results indicate i-DREAMS interventions significantly enhance driving behavior, with Neural Networks displaying superior performance among the algorithms considered.

Shared spaces for active mobility prioritize the safety and comfort of vulnerable road users by segregating them from motorized vehicles. However, the diverse speed regimes of pedestrians and cyclists can lead to encounters that may affect their comfort. In addition, the very perception of comfort may vary across individuals depending on their demographics, and therefore the determinants of comfort and their effects may not be fixed across all individuals. Despite these complexities, there is limited research in understanding the heterogeneous interactions between cyclists and other road users in shared spaces. To bridge this gap, we conducted an intercept survey complemented by an experimental section involving 594 cyclists in Sweden. This study focuses on gaining insights into cyclists' experiences, particularly their comfort levels during 'passing' and 'meeting' events with other road users in shared spaces. We then used the collected data to develop a random effect latent class ordered probit model to scrutinize the determinants of cycling comfort in passing and meeting scenarios. The latent class specification is employed to account for unobserved heterogeneity in the data. Findings reveal that female cyclists generally perceive less comfort compared to their male counterparts in both scenarios. Passing events have a more negative impact on older adults, leading to less comfort compared to younger cyclists. We also found that previous cycling experience increases comfort in shared facilities, particularly for older adults. These results highlight the intricate nature of perceived comfort in interactions, particularly concerning demographic characteristics, contributing to the promotion of user diversity in shared spaces.

Shared spaces for active mobility aim to offer safe and comfortable mobility for vulnerable road users by separating them from motorised vehicles. However, the distinct navigation characteristics of these users may increase the complexity of their interactions. The emergence of e-bikes which are faster and heavier than regular bikes has further increased this complexity. This study aims to shed light on the interdependency of e-bikes and pedestrians behaviours in shared spaces, and investigate how they influence each other's navigation. Through a controlled experiment in Lund, Sweden, data were collected on a total of 1520 trajectories of e-bike and pedestrians, their demographics and cycling experience. A simultaneous equation model was used to quantify the interactions between the participants. Results demonstrate significant correlations among variables, highlighting the model's capacity to effectively capturing the hypothesized interdependencies. The findings can inform the development of level-of-service indices and surrogate safety measures for shared spaces.

Existing models for correlating global mortality rates with underlying country-specific factors overlook the variations in the effects of these factors on mortality across different countries. These may arise from social, cultural, and political complexities which are usually not measurable and are therefore referred to as unobserved heterogeneity in the statistical literature. Unobserved heterogeneity leads to biased parameter estimates in the models, erroneous inferences about the effects of factors contributing to mortalities, and ultimately inefficient policies. In this paper, latent class modelling is proposed for capturing such unobserved heterogeneity on the cases of traffic mortality and COVID-19 mortality. The ‘pyramid’ model of safety management is used as a common framework for model formulation. The proposed latent class model is an extension of the Negative Binomial (NB) model used in risk epidemiology. The model is tested with data from 105 countries, retrieved from international databases, including socioeconomic, infrastructure, exposure, transport, and COVID-19 variables. The results suggest that there exist two (different) latent country classes in both causes of mortality. The probability of a country belonging to a certain latent class is a much more efficient metric of country membership than previous deterministic groupings (e.g. income or geographic). Variables such as the elderly population, the GDP per capita or the level of motorization, have different effects in different country classes; these effects are not identifiable by conventional statistical modelling. The impact of ignoring unobserved heterogeneity in country mortality modelling is shown by comparing the results with those of conventional NB models.

As automated vehicles (AVs) become increasingly popular, the question arises as to how cyclists will interact with such vehicles. This study investigated (1) whether cyclists spontaneously notice if a vehicle is driverless, (2) how well they perform a driver-detection task when explicitly instructed, and (3) how they carry out these tasks. Using a Wizard-of-Oz method, 37 participants cycled a designated route and encountered an AV multiple times in two experimental sessions. In Session 1, participants cycled the route uninstructed, while in Session 2, they were instructed to verbally report whether they detected the presence or absence of a driver. Additionally, we recorded participants' gaze behaviour with eye-tracking and their responses in post-session interviews. The interviews revealed that 30% of the cyclists spontaneously mentioned the absence of a driver (Session 1), and when instructed (Session 2), they detected the absence and presence of the driver with 93% accuracy. The eye-tracking data showed that cyclists looked more frequently and for longer at the vehicle in Session 2 compared to Session 1. Additionally, participants exhibited intermittent sampling of the vehicle, and they looked at the area in front of the vehicle when it was far away and towards the windshield region when it was closer. The post-session interviews also indicated that participants were curious, but felt safe, and reported a need to receive information about the AV's driving state. In conclusion, cyclists can detect the absence of a driver in the AV, and this detection may influence their perception of safety. Further research is needed to explore these findings in real-world traffic conditions.

Disruptions occur frequently in railway networks, requiring timetable adjustments, while causing serious delays and cancellations. However, little is known about the performance dynamics during disruptions nor the extent to which the resilience curve applies in practice. This paper presents a data-driven quantification approach for an ex-post assessment of the resilience of railway networks. Using historical traffic realization data in the Netherlands, resilience curves are reconstructed using a new composite indicator, and quantified for a large set of single disruptions. The values of the resilience metrics are compared across disruptions of different causes using Welch's ANOVA and the Games-Howell test. Additionally, representative resilience curves for each disruption cause are determined. Results show a significant heterogeneity in the shape of the resilience curves, even within disruptions of the same cause. The proposed approach represents a useful decision support tool for practitioners to assess disruptions dynamics and propose best measures to improve resilience.

Driver anticipation plays a crucial role in crashes along horizontal curves. Anticipation is related to road predictability and can be influenced by roadway geometric design. Therefore, it is essential to understand which geometric design elements can influence anticipation and cause the road to be (un)predictable. This exercise, however, is not straightforward because anticipation is individual-specific whereas road geometric design is location-specific; anticipation is latent and measuring it may not be trivial; anticipation may have several stages from the preceding tangent until the midst of the curve; and not all drivers anticipate in the same way and thus there may well be unobserved heterogeneity in the effect of anticipation on crash risk. Despite methodological advancements in crash risk modelling, there is no econometric model that can adequately explain the above complexities. This study aims to fill this gap by developing an econometric model with a new latent variable, named ‘predictability’ that is measured by individual-specific driving behaviour indicators and predicted by location-specific road geometric factors. The model is specified with random parameters to account for unobserved heterogeneity and is empirically tested by a unique dataset including detailed geometric design and driver behaviour data obtained for 156 curves in the Netherlands. Results indicate that higher exposure and uphill vertical grade are associated with increased likelihood of vehicle crashes along horizontal curves, whereas adequate superelevation and higher predictability are associated with decreased likelihood of those crashes. Pavement friction influences this likelihood too but it has varied effects. Road predictability is influenced by the differences in angle of horizontal curves, vertical grades, and width of consecutive road segments.

Automated vehicles have started to be integrated into the road transportation system and operate in a mixed traffic environment. To ensure a smooth and successful integration, it is vital to have a good understanding of the human factor challenges involved in the process, especially the issues related to other road users who will share roads with automated vehicles. The study focuses on conventional vehicle drivers’ acceptance of and interaction with fully automated vehicles (FAV). An online survey with experimental scenarios showing an FAV's lane-changing intention was designed to test the interaction responses of participants. The survey also collected the participants’ demographic information (e.g., age, gender, driving experience), self-reported general driving behaviours (e.g., errors, lapses and violations), past benchmark behaviour in the same situation and their acceptance of FAVs. The study recruited 838 participants in total, comprising 465 participants from Australia (216 males vs. 249 females) and 373 participants from China (172 males vs. 201 females). Ordered probit models were developed to predict three types of behavioural responses of drivers in the lane-changing scenario, i.e., positive, disregardful and aggressive responses. The results showed that older drivers, females, and drivers who had less driving experience were more likely to adopt positive interactions with FAVs than their counterparts. Drivers who reported frequent risky driving behaviours (e.g., aggressions, lapses and errors) were less likely to report positive interaction but more likely to report disregardful and aggressive interactions. Drivers reporting more positive/favourable attitudes and a higher trust toward FAVs demonstrated a higher possibility of positive interaction, and those with higher perceived behaviour control were more likely to restrain disregardful interaction. The study helps to form a greater understanding of conventional vehicle drivers’ perception of FAVs and the underlying factors that may influence their interaction behavioural tendency.

Despite the promised benefits, the introduction of Automated Vehicles (AVs) on roads will be confronted by many challenges, including public readiness to use those vehicles and share the roads with them. The risk profile of road users is a key determinant of their safety on roads. However, the relation of such risk profiles to road users’ perception of AVs is less known. This study aims to address the above research gap by conducting a cross-sectional survey to investigate the acceptance of Fully Automated Vehicles (FAVs) among different non-AV-user groups (i.e., pedestrians, cyclists, and conventional vehicle drivers). A total of 1205 road users in Queensland (Australia) took part in the study, comprising 456 pedestrians, 339 cyclists, and 410 drivers. The Theory of Planned Behaviour (TPB) is used as the theoretical model to examine road users’ intention towards sharing roads with FAVs. The risk profile of the participants derives from established behavioural scales and individual characteristics are also included in the acceptance model. The study results show that pedestrians reported lowest intention in terms of sharing roads with FAVs among the three groups. Drivers and cyclists in a lower risk profile group were more likely to report higher intention to share roads with FAVs than those in a higher risk profile group. As age increased, pedestrians were less likely to accept sharing roads with FAVs. Drivers who had more exposure time on roads were more likely to accept sharing roads with FAVs. Male drivers reported higher intention towards sharing roads than female drivers. Overall, the study provides new insights into public perceptions of FAVs, specifically from the non-AV-user perspective. It sheds light on the obstacles that future AVs may encounter and the types of road users that AV manufacturers and policymakers should consider closely. Specifically, groups such as older pedestrians and road users who engage in more risky behaviours might resist or delay the integration of AVs.

Fully automated vehicles (FAVs) have the potential to improve road safety and reduce traffic congestion and emissions. Most studies of acceptance of FAVs have focused on motor vehicle users, largely ignoring other road users, such as cyclists. This study investigates the factors that influence cyclists’ receptivity towards sharing roads with FAVs and their behavioural intentions in interactions with FAVs. The online survey collected information on participant demographics (e.g. age, gender, crash experience), self-reported on-road cycling behaviours (e.g. violations, errors, positive behaviours) and their receptivity towards sharing roads with FAVs (e.g. attitude, social norms, trust). Three typical cyclist-vehicle interaction scenarios were presented to test the cyclists’ intention to engage in self-protective behaviours (e.g. giving a hand signal, giving way or moving over) during the interaction with a FAV. Three hundred and fourteen Australian adults (106 females vs 208 males) who had ridden a bicycle at least once in the past year completed the survey. The results show that older cyclists and male cyclists had a lower receptivity towards sharing roads with FAVs than younger cyclists and female cyclists, respectively. Cyclists who reported being involved in a bicycle crash in the last two years and those who reported committing more errors on roads were more willing to share roads with FAVs. Cyclists who had a higher propensity to risky behaviours and positive behaviours were less likely to take intended self-protective behaviours during interaction with FAVs. Findings of the study provide some insights from the cyclist's perspective to facilitate the development and implementation of automated vehicles.

Run-off-road crashes are one of the most common crash types, especially in rural roadway environments contributing significantly to fatalities and severe injuries. These crashes are complex and multi-dimensional events, and factors like road geometry, driver behaviour, traffic characteristics and roadside features contribute to their occurrence, separately or interactively. Sudden changes in road geometry, in particular, can influence driver behaviour, and therefore, in developing a micro-level crash risk model for run-off-road crashes, one of the challenges is incorporating the effects of driver behaviour (disaggregated information) that may arise from the variations in road geometry (aggregated information). This study aims to examine the interaction between road geometry and driver behaviour through a set of measures for design consistency on two-lane rural roads. Multiple data sources, including crash data for 2014–18, traffic data, probe speed data and roadway geometric data, for twenty-three highways in Queensland, Australia, have been fused for this study. Seventeen types of design consistency measures with regard to alignment consistency, operating speed consistency and driving dynamics are tested. A run-off-road crash risk model is estimated by employing the Random Parameters Negative Binomial Lindley regression framework, which accounts for excess zeros in the crash counts and captures the effects of unobserved heterogeneity in the parameter estimates. Results indicate that the geometric design consistency capturing the interaction between driver behaviour and operational factors better predicts run-off-road crashes along rural highways. In addition, roadside attributes like clear zone width, infrastructures, terrain, and roadway remoteness also contribute to run-off-road crashes. The findings of the study provide a comprehensive understanding of the influence of variations in roadway geometry on driver behaviour and run-off-road crashes along rural highways.

Road safety research is largely focused on prediction and prevention of technical, human or organisational failures that may result in critical conflicts or crashes. Indicators of traffic risk aim to capture the passage to unsafe states. However, research in other industries has shown that it is meaningful to analyse safety along the whole spectrum of behaviours. Knowing the causes and patterns of “successful” interactions, rather than failures, could give new insights on the complexity of the system and the adaptability and resilience of its users in handling the inherent risks. The concept is known as Safety-II and has been extensively explored in the aviation, healthcare and process engineering domains. In this paper, we explore a new Safety-II paradigm for road safety research. We briefly review Safety-II applications in other sectors. We then present a Safety-II model for road safety, by means of an inverse version of Hyden's “safety pyramid”. Furthermore, we discuss a number of key road safety goals, theories, analysis methods and data sources and map them into a tentative taxonomy of Safety-I and Safety-II applications. It is concluded that there can be opportunities and benefits from adopting this new mindset, in order to complement existing approaches.

Driver's response to a pedestrian crossing requires braking, whereby both excess and inadequate braking is directly associated with crash risk. The highly anticipated connected environment aims to increase drivers’ situational awareness by providing advanced information and assisting them during critical driving tasks such as braking. Focussing on this crucial behaviour and combined with the promise of a connected environment, the objective of this study is to examine the braking behaviour of drivers in response to a pedestrian at a zebra crossing in a connected environment. Seventy-eight participants from diverse backgrounds performed this driving task in the CARRS-Q Advanced Driving Simulator in two randomised driving scenarios: a baseline scenario (without driving aids) and a connected environment (with driving aids) scenario. A Weibull accelerated failure time duration modelling approach is adopted to model the braking behaviour of drivers. In particular, this duration model is specified to capture the panel nature of the data and unobserved heterogeneity through correlated grouped random parameters with heterogeneity-in-the-means in the Bayesian framework. Results indicate that, for most drivers in the connected environment, it takes longer to reduce their speed with less speed variation and a larger safety margin. In addition, a decision tree analysis for the braking time suggests that for older drivers, when the distance to the zebra crossing is larger in the connected environment than that in the baseline scenario, braking time is likely to increase. The model also reveals that the braking time of female drivers is longer in the connected environment compared to that of male drivers. Overall, the connected environment is associated with increased braking time by providing advanced information, giving drivers additional time to smoothly reduce their speed in response to a pedestrian at a zebra crossing, and ultimately making the vehicle–pedestrian interaction safer.

Sleepiness is a common human factor among truck drivers resulting from sleep loss or time of day and causing impairment in vigilance, attention, and driving performance. While driver sleepiness may be associated with increased risk on the road, sleepy drivers may drive more cautiously as a result of risk-compensating behaviour. This endogeneity has been overlooked in the previous driver behaviour studies and may provide new insight into the effects of sleepiness on driving performance. In addition, the Karolinska Sleepiness Scale (KSS) has been widely used to quantify sleepiness. However, the KSS is a subjective self-reported measure and is reliant on honest reporting and understanding of the scale. An alternative way of quantifying sleepiness is using drivers’ heart rate and correlating it with their sleepiness. While recent advances in data collection technologies have made it possible to collect heart rate data in real-time and in an unobtrusive way, their application in measuring sleepiness particularly among truck drivers has been unexplored.

This study aims to address these gaps and contribute to analytic methods in road safety research by collecting truck drivers’ heart rate data in real-time, measuring sleepiness from those data, and using it in an instrumental variable modelling framework to investigate its effect on driving performance. To this end, a driving simulator experiment was conducted in Belgium and heart rate data were collected for 35 truck drivers via sensors installed on the steering wheel of the simulator. Additional demographic data were collected using a questionnaire before the experiment. An instrumental variable model consisting of a discrete binary logit and a continuous generalized linear model with grouped random parameters and heterogeneity in their means was then developed to study the effects of driver sleepiness on headway. Results indicate that age, years of holding driver licence, road type, type of truck transport, and weekly distance travelled are significantly associated with sleepiness among the participants of this study. Sleepy driving is associated with reduced headway for 30.5% of the drivers and increased headway for the other 69.5%, and night-time shift is associated with such varied effects. These findings indicate that there may be group- or context-specific risk patterns which cannot be explicitly addressed by hours of service regulations and therefore, transport operators, driver trainers and fleet managers should identify and handle such context-specific high risk patterns in order to ensure safe operations.

Despite the proven effectiveness of seatbelt use in reducing traffic casualties, not wearing a seatbelt still contributes to a substantial proportion of fatal crashes worldwide. This problem has raised the need to better understand factors contributing to seatbelt use, particularly in multi-occupant vehicles. Among these factors, behavioural determinants of seatbelt use are difficult to measure and their data are not readily available. These behavioural factors may have shared influences on vehicle occupants, causing their seatbelt use choices to be correlated. These complexities have prevented a comprehensive understanding of seatbelt use choices in the literature. This study aims to fill this gap by developing an econometric model that explains seatbelt use choices in multi-occupant vehicles. A set of binary logit models are constructed for seatbelt use choices and their utilities are correlated across vehicle occupants. A new latent variable representing the unobserved factors or ‘atmosphere’ of the vehicle is then incorporated into the model. The model is empirically tested using seatbelt use data from Tennessee, United States. Results indicate that vehicle body type and time of the day are significantly associated with seatbelt use. In addition, the collective seatbelt use in a vehicle is influenced by the unobserved atmosphere in the vehicle. Age, alcohol and drug consumption, higher proportion of old population and white racial mix, higher income per capita, and higher education levels are factors contributing to this latent atmosphere.

Driver sleepiness and fatigue are important contributors to many transport incidents and significantly increase crash risk. Recently, detection systems have been developed which aim to monitor the state of the driver and detect increasing levels of fatigue. However, there has been less focus on appropriate intervention strategies for drivers once fatigue and sleepiness have been detected. This paper describes the i-DREAMS fatigue intervention strategies, which aim to keep drivers within a safe driving zone. Interventions will be provided both in real-time and post-trip and can be customized to be used with a variety of transport modes. Real-time interventions will measure fatigue through trip duration, and driver sleepiness through heart-rate variability (HRV) information, obtained by means of sensors in the steering wheel or a wearable device, and attributed to Karolinska Sleepiness Score (KSS) bands. Thresholds for warnings will map onto phases of a ‘Safety Tolerance Zone’ and will be dynamic – changing as the driver state and driving situation develops. Post-trip interventions will aggregate data throughout the duration of the drive and aim to provide customized feedback and coping tips related to driver levels of fatigue and sleepiness, to improve driving behavior. Goals and challenges will add a gamified aspect to the post-trip interventions. The next stage of the development of the i-DREAMS fatigue intervention strategy is to test the concept in a series of simulator and field trials. Future research should explore acceptance and compliance of interventions and frequency of alerts.

Adoption of Automated Vehicles (AVs) within transport networks relies on the technology acceptance of not only AV users, but also other road users such as pedestrians. However, previous research has mostly focused on user acceptance of AVs and the receptivity of pedestrians towards AVs has been largely unexplored. This study aims to fill this gap by applying the Theory of Planned Behaviour (TPB), the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate pedestrians’ intentions to cross a road in front of a fully AV. To achieve this goal, a 20-minute online questionnaire was administered in Australia and data were collected from a total of 485 participants (average age = 35.35 years, 51.5% female). Bivariate correlation analysis and hierarchical regression models were then applied on the data to investigate the association between pedestrian attributes and their behavioural intentions. The findings revealed that the TPB and the UTAUT explained 46% and 43% of the variance in intentions to cross a road in front of a fully AV, respectively, with perceived behavioural control (PBC) and subjective/social norms the most significant unique predictors of intentions within the TPB and UTAUT, respectively. The TAM, however, only explained 35% of the variance in intentions to cross a road in front of a fully AV. When added into Step 2 of the hierarchical regression, age accounted for additional variance above the TAM predictors, indicating that younger participants reported higher intentions to cross a road in front of a fully AV than older participants. Age was not a significant predictor of intentions when entered with the predictors of the TPB and UTAUT. This study provides support for the use of these theoretical models to understand pedestrians’ acceptance of AVs.