Time-critical Situations Research Articles

Harnessing Artificial Intelligence for Wildlife Conservation

The rapid decline in global biodiversity demands innovative conservation strategies. This paper examines the use of artificial intelligence (AI) in wildlife conservation, focusing on the Conservation AI platform. Leveraging machine learning and computer vision, Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared cameras. The platform processes these data with convolutional neural networks (CNNs) and transformer architectures to monitor species, including those that are critically endangered. Real-time detection provides the immediate responses required for time-critical situations (e.g., poaching), while non-real-time analysis supports long-term wildlife monitoring and habitat health assessment. Case studies from Europe, North America, Africa, and Southeast Asia highlight the platform’s success in species identification, biodiversity monitoring, and poaching prevention. The paper also discusses challenges related to data quality, model accuracy, and logistical constraints while outlining future directions involving technological advancements, expansion into new geographical regions, and deeper collaboration with local communities and policymakers. Conservation AI represents a significant step forward in addressing the urgent challenges of wildlife conservation, offering a scalable and adaptable solution that can be implemented globally.

Deep heterogeneous joint architecture: A temporal frequency surrogate model for fuel performance codes

Fuel performance codes, such as Transuranus, predict fuel behavior and are used to ensure the safe operation of nuclear reactors. These codes are moderately time-consuming and affordable in many applications but may be limited in others, primarily when many fuel rods must be evaluated simultaneously. This work presents how the temporal neural network techniques, Temporal Convolutional Networks, and a Fourier Neural Operator can be combined to form a deep heterogeneous joint architecture as a surrogate model for fuel performance modeling in time-critical situations. We train the model using realistic power histories and corresponding outputs generated using the fuel performance code Transuranus. The ultimate result is a surrogate model for use in time-critical situations that take milliseconds to evaluate for thousands of fuel rods and have a mean test error of unseen data around a few percent.

Validating older adult patient's Medical Treatment Decision Maker's (MTDM). A retrospective observational study with follow-up phone interview transcript

A Medical Treatment Decision Maker (MTDM), also referred to as surrogate decision maker, by law, is to be appointed to make medical treatment decisions on behalf of a person who cannot make such decisions for themselves. In the Emergency Department (ED) and acute healthcare services, the clinicians’ (nurses and doctors) ability to contact MTDMs is essential for patient care, particularly in time-critical situations. Our primary objective was to review the verification process and assess the accuracy of MTDM contact numbers in the Health Information System (HIS) to assess compliance with legislation. We used a quantitative method with retrospective observational study design and follow-up phone interview transcript. One hundred and fifty-nine participants were randomly selected of whom 76 % had MTDM. Patient advancing age had statistically significant association with the number of call attempts made to reach the listed MTDM (P = 0.043; CI, -3.541 to -0.057) and the MTDM's consent to participate (p = 0.023).

What is the Evidence for Using Intranasal Medicine in the Prehospital Setting? A Systematic Review

Objectives Intranasal (IN) medications offer a safe non-invasive way to rapidly deliver drugs in situations where intravenous (IV) access and intramuscular (IM) administration is challenging or not feasible. In the prehospital setting, this can be an essential alternative in time critical situations including trauma management, seizures, and agitated patients. However, there is a paucity of evidence summarizing its efficacy in this environment. This systematic review aims to assess the current evidence supporting the use of IN medicine (midazolam, ketamine, fentanyl, morphine, glucagon, and naloxone) in the prehospital setting alone. Methods A systematic literature search (PROSPERO CRD42023440713) of PubMed, Web of Science, OVID Medline, “Cochrane Central Register of Controlled Trials,” Cochrane reviews and Embase was performed from inception to June 2023 to identify studies where IN medications were administered to patients in the prehospital setting. All randomized controlled trials, observational cohort studies, case series, and case reports were included. Papers not written in English, review articles, abstracts, and non-published data (including letters to the editor) were excluded. The methodological quality of the included studies was interpreted using the Cochrane risk of bias tool and rated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. No funding was received. Results From 4818 studies, 39 were included (seven for midazolam, five for ketamine, twelve for fentanyl, one for diamorphine, two for glucagon, and twelve for naloxone). A total of 24,097 patients were treated with IN medications across all the studies. There were five moderate quality, four low quality, and thirty very low quality studies. The potential efficacy of IN fentanyl and ketamine was demonstrated consistently throughout the studies with less clear evidence for midazolam, morphine, glucagon, and naloxone. This review was severely limited by the study quality, with most studies demonstrating “high concerns” for bias. Conclusions Prehospital IN medication administration has wide-ranging potential, particularly for administering analgesia. There are likely to be certain populations, for example, pediatrics, that will benefit the most, although conclusions are limited by the quality of evidence currently available. We encourage additional research in this area, particularly with robust prospective double-blind RCTs.

Decision making in prehospital and paramedic practice: part 1

This first continuing professional development article in this 2-part series on decision-making explores this process in prehospital and paramedic practice and the complex world of how clinicians make decisions in often challenging and time-critical situations in prehospital settings. It gives a brief overview of the neuroanatomy involved in decision-making, problem-solving and critical thinking as well as Bloom's taxonomy and its application to prehospital practice. While it is outside the remit of this paper to incorporate all theories regarding decision-making, it will explore some key concepts and frameworks around it.

Tailored and universal parallel transmit broadband pulses for homogeneous 3D excitation of the human heart at 7T.

To research and evaluate the performance of broadband tailored kT-point pulses (TP) and universal pulses (UP) for homogeneous excitation of the human heart at 7T. Relative 3D -maps of the thorax were acquired from 29 healthy volunteers. TP and UP were designed using the small-tip-angle approximation for a different composition of up to seven resonance frequencies. TP were computed for each of the 29 -maps, and UPs were calculated using 22 -maps and tested in seven testcases. The performance of the pulses was analyzed using the coefficient of variation (CV) in the 3D heart volumes. The 3D gradient-echo (GRE) scans were acquired for the seven testcases to qualitatively validate the -predictions. Single- and double-frequency optimized pulses achieved homogeneity in flip angle (FA) for the frequencies they were optimized for, while the broadband pulses achieved uniformity in FA across a 1300 Hz frequency range. Broadband TP and UP can be used for homogeneous excitation of the heart volume across a 1300 Hz frequency range, including the water and the main six fat peaks, or with longer pulse durations and higher FAs for a smaller transmit bandwidth. Moreover, despite large inter-volunteer variations, broadband UP can be used for calibration-free 3D heart FA homogenization in time-critical situations.

How consistent are pre-alert guidelines? A review of UK ambulance service guidelines.

Ambulance pre-alerts are used to inform receiving emergency departments (EDs) of the arrival of critically unwell or rapidly deteriorating patients who need time-critical assessment or treatment immediately upon arrival. Inappropriate use of pre-alerts can lead to EDs diverting resources from other critically ill patients. However, there is limited guidance about how pre-alerts should be undertaken, delivered or communicated. We aimed to map existing pre-alert guidance from UK NHS ambulance services to explore consistency and accessibility of existing guidance. We contacted all UK ambulance services to request documentation containing guidance about pre-alerts. We reviewed and mapped all guidance to understand which conditions were recommended for a pre-alert and alignment with Association of Ambulance Chief Executives (AACE) and Royal College of Emergency Medicine (RCEM) pre-alert guidance. We reviewed the language and accessibility of guidance using the AGREE II tool. We received responses from 15/19 UK ambulance services and 10 stated that they had specific pre-alert guidance. We identified noticeable variations in conditions declared suitable for pre-alerts in each service, with a lack of consistency within each ambulance service's own guidance, and a lack of alignment with the AACE/RCEM pre-alert guidance. Services listed between four and 45 different conditions suitable for pre-alert. There were differences in physiological thresholds and terminology, even for conditions with established care pathways (e.g. hyperacute stroke, ST segment elevation myocardial infarction). Pre-alert criteria were typically listed in several short sections in lengthy handover procedure policy documents. Documents appraised were of poor quality with low scores below 35% for applicability and overall. There is a clear need for ambulance services to have both policies and tools that complement each other and incorporate the same list of pre-alertable conditions. Clinicians need a single, easily accessible document to refer to in a time-critical situation to reduce the risk of making an incorrect pre-alert decision.

I-AVR: IoT-Based Ambulatory Vitals Monitoring and Recommender System

In this paper, we propose and implement i-AVR, an Internet of Things (IoT)-based critical-aware system for point-of-care recommendation during ambulatory in-transits. The delay due to ambulances stuck in traffic congestion, disruptive roadways, and far-away hospitals restrain the smooth ambulance services. Therefore, in order to assist the time-critical scenario of a hospital-bound patient, we consider a guidance system to address the necessity. Moreover, these patients require continuous vitals monitoring, which may vary with the progress of time, to reduce the response time upon reaching the destination. The implemented i-AVR comprises two units – a portable healthcare unit and an android navigation unit. The healthcare unit aims to compute the criticality index of the en-route patient and recommend the nearest healthcare center while the navigation unit recommends the convenient route in case of any anomaly in vitals. We show the effectiveness of i-AVR regarding network performance while highlighting the response time of the system. We observe the system response time for computation in orders of seconds and inter-unit communication in milliseconds. Eventually, this analysis indicates the effectiveness of i-AVR in providing quick decisions during the time-critical situations. Our implementation provides essential intervention towards IoT-based healthcare technologies.

Failure Pressure Prediction of Corroded High-Strength Steel Pipe Elbow Subjected to Combined Loadings Using Artificial Neural Network

There is no reliable failure pressure assessment method for pipe elbows, specifically those subjected to internal pressure and axial compressive stress, other than time-consuming numerical methods, which are impractical in time-critical situations. This paper proposes a set of empirical equations, based on Artificial Neural Networks, for the failure pressure prediction of pipe elbows subjected to combined loadings. The neural network was trained with data generated using the Finite Element Method. A parametric analysis was then carried out to study the failure behaviour of corroded high-strength steel subjected to combined loadings. It was found that defect depth, length, spacing (longitudinal), and axial compressive stress greatly influenced the failure pressure of a corroded pipe elbow, especially for defects located at the intrados, with reductions in failure pressure ranging from 12.56–78.3%. On the contrary, the effects of circumferential defect spacing were insignificant, with a maximum of 6.78% reduction in the failure pressure of the pipe elbow. This study enables the failure pressure prediction of corroded pipe elbows subjected to combined loadings using empirical equations. However, its application is limited to single, longitudinally interacting, and circumferentially interacting defects with the specified range of parameters mentioned in this study.

User-Centered Design (UCD) of Time-Critical Weather Alert Application

Weather alert applications can save precious lives in time-critical risk situations; however, even the most widely used applications may fall short in intuitive interface and content design, possibly due to limitations in the users participation in the design process and in the users range considered. The objective of this study was to investigate whether the application of UCD principles and usability guidelines can improve the use of and satisfaction of time-critical weather alert apps by the public and or expert users. A prototype of a UCD-based weather alert application was developed and evaluated. Initially, thirty-two voluntaries participated in the identification of the important features that lead to the development of the porotype, and then the prototype was tested with another eighty participants (40 young and 40 elderly). The prototype includes five enhancements: auto-suggested location search, an all-inclusive interface for weather forecasts, message alert, visual and intuitive map settings, and minimalism-oriented alert settings. The enhanced functionality was compared to similar functionality in existing commercial weather applications. Effectiveness (completion rate, error count, error severity, and error cause), efficiency (time to completion), and satisfaction (post-task and post-test surveys) were measured. The results showed the enhancements significantly improved performance and satisfaction across both age groups compared to equivalent functionality in the existing app. The Mann-Whitney U test showed a statistically significant difference (p<0.001) in task satisfaction and number of errors between the two apps for all tasks. The Mann-Whitney U test showed a significant difference (p<0.001) in the across all tasks between the two apps Also, overall, young people with existing apps outperformed elderly, and both young and elderly with enhanced apps performed very high. Therefore, the enhancements implemented through the UCD process and usability guidelines significantly improved performance and satisfaction across both age groups to facilitate timely action necessary during a crisis.

Effect of non-return valves on the time-of-arrival of new medication in a patient after syringe exchange in an infusion set-up.

The presence of a non-return valve in an infusion set-up is expected to affect the time-of-arrival of new medication in a patient after syringe exchange. Using Computational Fluid Dynamics (CFD) we have studied theflow through a typical non-return valve, focusing on two separate effects: (A)the overall delay in the time-of-arrival, and (B)timing effects due to the distortion of the Poiseuille flow profile in the non-return valve. The results show that (A)the additional delay in time-of-arrival of new medication, caused by the non-return valve alone, corresponds to the delay that would be caused by 11.2cm of extra infusion line instead of the valve, and that (B)the non-Poiseuille flow profile inside the non-return valve gives rise to an extra slow wash-out of the last portion ofthe remnant fluid of the old medication. We conclude that awareness of these extra delays may be important forclinicians in certain time-critical situations.

Theory-based approach for assessing cognitive load during time-critical resource-managing human–computer interactions: an eye-tracking study

Computerized systems are taking on increasingly complex tasks. Consequently, monitoring automated computerized systems is becoming increasingly demanding for human operators, which is particularly relevant in time-critical situations. A possible solution might be adapting human–computer interfaces (HCI) to the operators’ cognitive load. Here, we present a novel approach for theory-based measurement of cognitive load based on tracking eye movements of 42 participants while playing a serious game simulating time-critical situations that required resource management at different levels of difficulty. Gaze data was collected within narrow time periods, calculated based on log data interpreted in the light of the time-based resource-sharing model. Our results indicated that eye fixation frequency, saccadic rate, and pupil diameter significantly predicted task difficulty, while performance was best predicted by eye fixation frequency. Subjectively perceived cognitive load was significantly associated with the rate of microsaccades. Moreover our results indicated that more successful players tended to use breaks in gameplay to actively monitor the scene, while players who use these times to rest are more likely to fail the level. The presented approach seems promising for measuring cognitive load in realistic situations, considering adaptation of HCI.

'Today Was Probably One of the Most Challenging Workdays I've Ever Had': Doing Remote Qualitative Research with Hospital Doctors During the COVID-19 Pandemic.

In this article we outline how a team of qualitative researchers responded to the challenging circumstances of the COVID-19 pandemic, describing how we successfully and speedily adopted remote/digital methods to research the experiences of hospital doctors. In 2020, we used Zoom to conduct qualitative interviews with 48 hospital doctors; in 2021, we used Zoom and WhatsApp to conduct a Mobile Instant Messaging Ethnography with 28 hospital doctors. We explain how we adapted to a virtual setting and provide clear insights (case study vignettes) into the additional demands on researchers and respondents, in particular, the impact on the research team. Finally, we analyse the positive and negatives of using remote qualitative methods and highlight the potential of hybrid data collection models that combine remote and face-to-face methods. We also highlight our success in communicating findings to a policy audience, important in time-critical situations, such as the COVID-19 pandemic.

“It seems to be some kind of an accident”: Perception and team decision-making in time critical situations

This study examines the decision-making processes of military observer trainee teams as they encounter a simulated, ‘life-threatening’ incident during a car-patrolling exercise. The study takes place in the context of a course which trains military officers to serve in UN peacekeeping operations as unarmed military observers. The data consist of audio-video recordings of eight trainee teams inside and around their patrol cars during a patrolling exercise, as well as of ethnographic field notes and observations. We use conversation analysis to examine what happens between the first noticing of the incident and the decision regarding the next action, and how the trainees work together to form a diagnosis of the situation and choose the appropriate action. The results show how the trainees make a first general interpretation on the situation immediately after its initial noticing and make visible their assessment of the incident site as either safe or unsafe to approach. These interpretations are crucial, as an incorrect interpretation often leads to action that puts the team in danger. The results also show how earlier shared events can affect the decision-making process, as they are indicated as points of comparison on which a team may base their interpretation of the current situation.

AvaLife-A New Multi-Disciplinary Approach Supported by Accident and Field Test Data to Optimize Survival Chances in Rescue and First Aid of Avalanche Patients.

Snow sports in the backcountry have seen a steep increase in popularity, and therefore preparedness for efficient companion and organized rescue is important. While technical rescue skills are widely taught, there is a lack of knowledge regarding first aid for avalanche patients. The stressful and time-critical situation for first responders requires a rule-based decision support tool. AvaLife has been designed from scratch, applying mathematical and statistical approaches including Monte Carlo simulations. New analysis of retrospective data and large prospective field test datasets were used to develop evidence-based algorithms exclusively for the avalanche rescue environment. AvaLife differs from other algorithms as it is not just a general-purpose CPR algorithm which has been slightly adapted for the avalanche patient. The sequence of actions, inclusion of the ≥150 cm burial depth triage criterion, advice to limit CPR duration for normothermic patients to 6 min in case of multiple burials and shortage of resources, criteria for using recovered subjects as a resource in the ongoing rescue, the adapted definition of “injuries incompatible with life”, reasoning behind the utmost importance of rescue breaths, as well as the updated BLS-iCPR algorithm make AvaLife useful in single and multiple burial rescue. AvaLife is available as a companion rescue basic life support (BLS) version for the recreational user and an advanced companion and organized rescue BLS version for guides, ski patrols and mountain rescuers. AvaLife allows seamless interoperability with advanced life support (ALS) qualified medical personnel arriving on site.

Investigating Differences in Behavior and Brain in Human-Human and Human-Autonomous Vehicle Interactions in Time-Critical Situations.

Some studies provide evidence that humans could actively exploit the alleged technological advantages of autonomous vehicles (AVs). This implies that humans may tend to interact differently with AVs as compared to human driven vehicles (HVs) with the knowledge that AVs are programmed to be risk-averse. Hence, it is important to investigate how humans interact with AVs in complex traffic situations. Here, we investigated whether participants would value interactions with AVs differently compared to HVs, and if these differences can be characterized on the behavioral and brain-level. We presented participants with a cover story while recording whole-head brain activity using fNIRS that they were driving under time pressure through urban traffic in the presence of other HVs and AVs. Moreover, the AVs were programmed defensively to avoid collisions and had faster braking reaction times than HVs. Participants would receive a monetary reward if they managed to finish the driving block within a given time-limit without risky driving maneuvers. During the drive, participants were repeatedly confronted with left-lane turning situations at unsignalized intersections. They had to stop and find a gap to turn in front of an oncoming stream of vehicles consisting of HVs and AVs. While the behavioral results did not show any significant difference between the safety margin used during the turning maneuvers with respect to AVs or HVs, participants tended to be more certain in their decision-making process while turning in front of AVs as reflected by the smaller variance in the gap size acceptance as compared to HVs. Importantly, using a multivariate logistic regression approach, we were able to predict whether the participants decided to turn in front of HVs or AVs from whole-head fNIRS in the decision-making phase for every participant (mean accuracy = 67.2%, SD = 5%). Channel-wise univariate fNIRS analysis revealed increased brain activation differences for turning in front of AVs compared to HVs in brain areas that represent the valuation of actions taken during decision-making. The insights provided here may be useful for the development of control systems to assess interactions in future mixed traffic environments involving AVs and HVs.

The effects of takeover request lead time on drivers’ situation awareness for manually exiting from freeways: A web-based study on level 3 automated vehicles

Conditional automation systems allow drivers to turn their attention away from the driving task in certain scenarios but still require drivers to gain situation awareness (SA) upon a takeover request (ToR) and resume manual control when the system is unable to handle the upcoming situation. Unlike time-critical takeover situations in which drivers must respond within a relatively short time frame, the ToRs for non-critical events such as exiting from a freeway can be scheduled way ahead of time. It is unknown how the ToR lead time affects driver SA for resuming manual control and when to send the ToR is most appropriate in non-critical takeover events. The present study conducted a web-based, supervised experiment with 31 participants using conditional automation systems in freeway existing scenarios while playing a mobile game. Each participant experienced 12 trials with different ToR lead times (6, 8, 10, 12, 14, 16, 18, 20, 25, 30, 45, and 60 s) for exiting from freeways in a randomized order. Driver SA was measured by using a freeze probe technique in each trial when the participant pressed the spacebar on the laptop to simulate the takeover action. Results revealed a positive effect of longer ToR lead times on driver SA for resuming control to exit from freeways and the effect leveled off at the lead time of 16–30 s. The participants tended to postpone their takeover actions further when they were given a longer ToR lead time and it did not level off up to 60 s. Nevertheless, not all drivers waited till the last moment to take over AVs even though they did not get sufficient SA. The ToR lead time of 16–30 s was recommended for better SA; and it could be narrowed down to 25–30 s if considering the subjective evaluations on takeover readiness, workload, and trust. The findings provide implications for the future design of conditional automation systems used for freeway driving.

Rapid development and implementation of a behaviour change strategy to improve COVID-19 personal protective equipment use in a regional Australian emergency department

ObjectiveTo identify barriers to, describe the development of and evaluate the implementation of a behavioural theory informed strategy to improve staff personal protective equipment (PPE) compliance during COVID-19 in a regional Australian Emergency Department. MethodsBarriers to PPE use were identified through staff consultation then categorised using the Theoretical Domains Framework. The Behaviour Change Wheel was used to develop a strategy to address the barriers to PPE compliance. The strategy was refined and endorsed by the site COVID taskforce. Data were collected through direct observation. Descriptive statistics were used to summarise PPE compliance and inductive content analysis for free text data of staff behaviours. Results73 barriers were identified, mapped to 9 intervention functions and 42 behaviour change techniques. The predominant mechanisms were: (1) Executive communication reinforcing policy and consequences; (2) implementation of a PPE Marshal; (3) face to face reinforcement / modeling; (4) environmental restructuring including electronic medical record modifications. The PPE Marshal observed 281 PPE activities. PPE compliance varied between 47.9% (Buddy check) and 91.8% (Bare below elbow). The PPE Marshal intervened on 121 occasions, predominantly through buddying, explaining and demonstrating correct PPE use, most frequently with medical staff (72%). ConclusionWe describe an evidence-based process to overcome barriers to PPE compliance that maximize safe work practice in a time critical situation. Staff require enabling, access to equipment and reinforcement to use PPE correctly.

Other road users’ adaptations to increase safety in response to older drivers’ behaviour

Changes in physical and cognitive abilities not only challenge the driving ability of older adults, in some situations age-related changes in driving behaviour require other road users to adapt their behaviour to maintain a safe traffic situation. In this study, we aimed to map age-related differences in driving behaviour and assess the impact on other road users. A group younger and a group older adults drove four different routes containing challenging situations (e.g., merging into motorway traffic) in a driving simulator while measures of driving behaviour were collected. Other road users’ deceleration responses to the driver’s behaviour were also collected as a measure of behavioural adaptation. Our results showed similar driving performance between young and older drivers when task complexity was low, but reduced performance in older drivers when tasks requirements increased. Lower driving speed and longer waiting times that were observed in older drivers can be interpreted as compensatory behaviour aimed at creating more time to lower task requirements. Crucially, in a non-time critical situation this compensatory behaviour was found to be successful, however in a time-critical situation (merging onto a motorway) this strategy had negative side effects because other road users had to decelerate in order to keep a safe distance. Our results show the importance of anticipation and adaptation by other road users for the success of older driver’s strategies and traffic safety.

Evaluation of WGS performance for bacterial pathogen characterization with the Illumina technology optimized for time-critical situations.

Whole genome sequencing (WGS) has become the reference standard for bacterial outbreak investigation and pathogen typing, providing a resolution unattainable with conventional molecular methods. Data generated with Illumina sequencers can however only be analysed after the sequencing run has finished, thereby losing valuable time during emergency situations. We evaluated both the effect of decreasing overall run time, and also a protocol to transfer and convert intermediary files generated by Illumina sequencers enabling real-time data analysis for multiple samples part of the same ongoing sequencing run, as soon as the forward reads have been sequenced. To facilitate implementation for laboratories operating under strict quality systems, extensive validation of several bioinformatics assays (16S rRNA species confirmation, gene detection against virulence factor and antimicrobial resistance databases, SNP-based antimicrobial resistance detection, serotype determination, and core genome multilocus sequence typing) for three bacterial pathogens ( Mycobacterium tuberculosis , Neisseria meningitidis , and Shiga-toxin producing Escherichia coli ) was performed by evaluating performance in function of the two most critical sequencing parameters, i.e. read length and coverage. For the majority of evaluated bioinformatics assays, actionable results could be obtained between 14 and 22 h of sequencing, decreasing the overall sequencing-to-results time by more than half. This study aids in reducing the turn-around time of WGS analysis by facilitating a faster response in time-critical scenarios and provides recommendations for time-optimized WGS with respect to required read length and coverage to achieve a minimum level of performance for the considered bioinformatics assay(s), which can also be used to maximize the cost-effectiveness of routine surveillance sequencing when response time is not essential.