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The purpose of this paper is to characterize distracted driving by quantifying the response time and response intensity to an emergency stop using the driver’s physiological states.

Field tests with 17 participants were conducted in the connected and automated vehicle test field. All participants were required to prioritize their primary driving tasks while a secondary nondriving task was asked to be executed. Demographic data, vehicle trajectory data and various physiological data were recorded through a biosignalsplux signal data acquisition toolkit, such as electrocardiograph for heart rate, electromyography for muscle strength, electrodermal activity for skin conductance and force-sensing resistor for braking pressure.

This study quantified the psychophysiological responses of the driver who returns to the primary driving task from the secondary nondriving task when an emergency occurs. The results provided a prototype analysis of the time required for making a decision in the context of advanced driver assistance systems or for rebuilding the situational awareness in future automated vehicles when a driver’s take-over maneuver is needed.

The hypothesis is that the secondary task will result in a higher mental workload and a prolonged reaction time. Therefore, the driver states in distracted driving are significantly different than in regular driving, the physiological signal improves measuring the brake response time and distraction levels and brake intensity can be expressed as functions of driver demographics. To the best of the authors’ knowledge, this is the first study using psychophysiological measures to quantify a driver’s response to an emergency stop during distracted driving.

Under different ground motion excitation modes, the spatial coupling effect of seismic response for the arch bridge with thrust, seismic weak parts and the internal force components of the control section of main arch ribs are analyzed.

Taking a 490 m deck type railway steel truss arch bridge as the background, the dynamic calculation model of the whole bridge was established by SAP2000 software. The seismic response analyses under one-, two- and three-dimension (1D, 2D and 3D) uniform ground motion excitations were carried out.

For the steel truss arch bridge composed of multiple arch ribs, any single direction ground motion excitation will cause large axial force in the chord of arch rib. The axial force caused by transverse and vertical ground motion excitation in the chord of arch crown area is 1.4–3.6 times of the corresponding axial force under longitudinal seismic excitation. The in-plane bending moment caused by the lower chord at the vault is 4.2–5.5 times of the corresponding bending moment under the longitudinal seismic excitation. For the bottom chord of arch rib, the arch foot is the weak part of earthquake resistance, but for the upper chord of arch rib, the arch foot, arch crown and the intersection of column and upper chord can all be the potential earthquake-resistant weak parts. The normal stress of the bottom chord of the arch rib under multidimensional excitation is mainly caused by the axial force, but the normal stress of the upper chord of the arch rib is caused by the axial force, in-plane and out of plane bending moment.

The research provides specific suggestions for ground motion excitation mode and also provides reference information for the earthquake-resistant weak part and seismic design of long-span deck type railway steel truss arch bridges.

Facing the diverse needs of large-scale customers, based on available railway service resources and service capabilities, this paper aims to research the design method of railway freight service portfolio, select optimal service solutions and provide customers with comprehensive and customized freight services.

Based on the characteristics of railway freight services throughout the entire process, the service system is decomposed into independent units of service functions, and a railway freight service combination model is constructed with the goal of minimizing response time, service cost and service time. A model solving algorithm based on adaptive genetic algorithm is proposed.

Using the computational model, an empirical analysis was conducted on the entire process freight service plan for starch sold from Xi'an to Chengdu as an example. The results showed that the proposed optimization model and algorithm can effectively guide the design of freight plans and provide technical support for real-time response to customers' diversified entire process freight service needs.

With the continuous optimization and upgrading of railway freight source structure, customer demands are becoming increasingly diverse and personalized. Studying and designing a reasonable railway freight service plan throughout the entire process is of great significance for timely response to customer needs, improving service efficiency and reducing design costs.

Technological developments and new customer expectations of immediacy have driven businesses to adopt on-demand service models. The purpose of this paper is to study the characteristics of a range of on-demand services in order to better understand the meaning of “on-demand” and its implications for service management. This enables the on-demand service logic to be applied to other service contexts, where it may add value for customers.

The study starts with a focused literature review and continues with a multiple case study methodology, as the on-demand service concept is in the early stages of theory development. Seven cases were studied, based on a maximum variation sampling strategy.

The results show that on-demand services are characterized by three interrelated characteristics: being highly available, responsive and scalable. Analysis further reveals that on-demand services display differences within the conceptual boundaries of these characteristics, i.e. they vary in terms of their availability, responsiveness and scalability.

Drawing on these findings, the study contributes to the service literature by being the first to specifically conceptualize and define the on-demand services concept and reveal three key characteristics that clarify the distinctive nature of this service type. Accordingly, on-demand services are clearly differentiated from other services. Additionally, the paper discusses the variety within on-demand services and develops an on-demand service continuum that gives detailed insights into the conceptual variations within such services.

Vertical lift (VL) assets are vital and expensive resources in humanitarian missions. What and where supplies are needed evolves in short time following a disaster. The purpose of this paper is to offer analysis to understand the range of capabilities of these assets.

The authors use scenario analysis to investigate the tradeoff between two key capabilities of VL, agility and speed. The authors do this by generating loads and distances randomly, based on historical data. In post hoc analysis, based on different factors, the authors investigate the impact of configuration of Expeditionary Strike Force (ESG) on providing disaster relief.

The authors find the most effective deployment of VL in a HADR mission is in supplying essentials to victims in a focused region. Delivering sustainment requirements leads to substantial shortfall for survival needs. If the configuration of the ESGs were changed for HADR, it would better-meet the demand.

Cargo capacity is modeled assuming every aircraft type was equal, in terms of mean and variance of cargo-capacity utilization. Detailed information on cargo-bay configurations was beyond the scope of our model and data. However, this means the benefit of standardizing cargo load-outs and the variability associated with randomized load-outs may be understated in the results.

The analysis presents decision-makers with projections of VL asset performance in the early stages of disaster relief, to assist in planning and contingency planning.

This research deals exclusively with the most critical but expensive capabilities for HADR: VL. The in-depth analysis illustrates the limitations and benefits of this capability.

Basic capturing of emotion on user experience of web applications and browsing is important in many ways. Quite often, online user experience is studied via tangible measures such as task completion time, surveys and comprehensive tests from which data attributes are generated. Prediction of users’ emotion and behaviour in some of these cases depends mostly on task completion time and number of clicks per given time interval. However, such approaches are generally subjective and rely heavily on distributional assumptions making the results prone to recording errors. This paper aims to propose a novel method – a window dynamic control system – that addresses the foregoing issues.

Primary data were obtained from laboratory experiments during which 44 volunteers had their synchronized physiological readings – skin conductance response, skin temperature, eye movement behaviour and users activity attributes taken by biosensors. The window-based dynamic control system (PHYCOB I) is integrated to the biosensor which collects secondary data attributes from these synchronized physiological readings and uses them for two purposes: for detection of both optimal emotional responses and users’ stress levels. The method’s novelty derives from its ability to integrate physiological readings and eye movement records to identify hidden correlates on a webpage.

The results from the analyses show that the control system detects basic emotions and outperforms other conventional models in terms of both accuracy and reliability, when subjected to model comparison – that is, the average recoverable natural structures for the three models with respect to accuracy and reliability are more consistent within the window-based control system environment than with the conventional methods.

Graphical simulation and an example scenario are only provided for the control’s system design.

The novelty of the proposed model is its strained resistance to overfitting and its ability to automatically assess user emotion while dealing with specific web contents. The procedure can be used to predict which contents of webpages cause stress-induced emotions to users.

Following Hovland’s persuasion theory, this paper aims to develop a conceptual model and analyzes characteristics of online political deliberation behavior from three aspects (i.e. information, situation and manager). Based on the whole interactive process of online political deliberation, this paper aims to reveal the key points that affect the response effect of the government from the persuasive perspective of online political consultation.

Based on more than 40,000 netizens’ posts and government responses from 2011 to the first half of 2019 of the Chinese political platform, this paper used the text analysis and machine learning methods to extract measurement variables of online political deliberation characteristics and the econometrics analysis method to conduct empirical research.

The results showed that the textual information, political environment and identity of the political objects affect the effectiveness of government response. Furthermore, for different position categories of political officials, the length of political texts, topic categories and emotional tendencies have different effects on the response effectiveness. Additionally, the effect of political time on the effectiveness of response differs.

The findings will help ascertain the characteristics of online political deliberation behavior that affect how effective government response is and provide a theoretical basis for why the public should express their political concerns.

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

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

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

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

This study aims to investigate the correlation between neural indexes of attention and behavioral indexes of attention and detect the most informative period of brain activity in which the strongest correlation with attentive performance (behavioral index) exists. Finally, to further validate the findings, this paper aims at the prediction of different levels of attention function based on the attention score obtained from repeatable battery for the assessment of neurophysiological status (RBANS).

The present paper analyzes electroencephalogram (EEG) signals recorded by a single prefrontal channel from 105 elderly subjects while they were responding to Stroop color test which is an attention-demanded task. Beside Stroop test, subjects also performed RBANS which provides their level of functionality in different domains including attention. After data acquisition (EEG during Stroop test and RBANS attention score), the authors extract the spectral features of EEG as neural indexes of attention and subjects’ reaction time in response to Stroop test as behavioral index of attention. Then, they explore the correlation between these post-cue frequency band oscillations of EEG with elderly response time (RT). Next, the authors exploit these findings to classify RBANS attention score.

The observations of this study suggest that there is significant negative correlation between alpha gamma ratio (AGR) and RT (p < 0.0001), theta beta ratio (TBR) is positively correlated with subjects’ RT (p < 0.0001), these correlations are stronger in a 500ms period right after triggering the cue (question onset in Stroop test), and 4) TBR and AGR can be effectively used to predict RBANS attention score.

Because of the experiment design, the pre-cue EEG of the next trail was very much overlapped with the post-cue EEG of the current trail. Therefore, the authors could analyze only post-cue EEG. In future study, it would be interesting to investigate the predictability of subject’s future performance from pre-cue EEG and mental preparation.

This study provides an insight into the research on detection of human attention level from EEG instead of conventional neurophysiological tests. It has also potential to be used in implementation of feasible and efficient EEG-based brain computer interface training systems for elderly.

To the best of the authors’ knowledge, this study is among very few attempts for early prediction of cognitive decline in the domain of attention from brain activity (EEG) instead of conventional tests which are prone to human errors.

This paper aims to reveal the philanthropy landscape processes for dealing with disasters and examine the influence of lean thinking in managing philanthropy for disasters. It sought to leverage continuous improvement and maximise disaster response and humanitarian logistics efficiency.

A sequential quantitative, qualitative research methods strategy was utilised involving data collection with literary analysis and two sets of online surveys with 212 NGO staff members in Malawi, Mozambique, South Africa and Zimbabwe. In addition, in-depth key informant interviews were conducted with 23 staff members at various management levels from these countries.

The study found that lean thinking had a positive, statistically valid influence at a 95% confidence level. Community incorporation, government support and collaboration with other philanthropic organisations were critical success factors. When lean thinking tools were applied (any tools), philanthropic organisations experienced waste reduction and value addition, where waste reduction accounted for 67% variation, and value-addition accounted for 58%, respectively. These were the same benefits experienced in other industries, thus, justifying lean thinking's applicability in the non-profit sector. Lean was most helpful about the communication and duplication of efforts challenges humanitarian or philanthropic organisations face when responding to a disaster.

The study equips leaders and philanthropic organisations with suggestions to manage and respond to disasters in a lean and effective manner. The study helps philanthropy leaders rethink their funding and response models to pursue lean policies catering to humanitarian organisations and the communities they serve.

The study closes significant gaps in the literature and practice by adopting a multi-sectoral lens that borrows from business and manufacturing tools into a non-profit context. It enables documentation of processes and logistical management by philanthropy organisations for continuous improvement and elimination of waste to ensure efficiency in the philanthropic role of alleviating the impact of disasters. The study also affirms the need for philanthropic organisations to incorporate community feedback, use lean tools to collaborate with other responding organisations and work closely with the local authorities to fulfil the government's supportive role: the primary source and executor in disaster response.