This paper studies the spatio-temporal dynamics of a diffusive plant-sulphide model with toxicity delay. More specifically, the effects of discrete delay and distributed delay on the dynamics are explored, respectively. The deep analysis of eigenvalues indicates that both diffusion and delay can induce Hopf bifurcations. The normal form theory is used to set up an exact formula that determines the properties of Hopf bifurcation in a diffusive plant-sulphide model. A sufficiently small discrete delay does not affect the stability and a sufficiently large discrete delay destabilizes the system. Nonetheless, a sufficiently small or large distributed delay does not affect the stability. Both delays cause instability by inducing Hopf bifurcation rather than Turing bifurcation.

Cannibalism is often an extreme interaction in the animal species to quell competition for limited resources. To model this critical factor, we improve the predator–prey model with nonlocal competition effect by incorporating the cannibalism term, and different kernels for competition are considered in this model numerically. We give the critical conditions leading to the double Hopf bifurcation, in which the gestation time delay and the diffusion coefficient were selected as the bifurcation parameters. The innovation of the work lies near the double Hopf bifurcation point, and the stable homogeneous and inhomogeneous periodic solutions can coexist. The theoretical results of the extended centre manifold reduction and normal form method are in good agreement with the numerical simulation.

Reflecting the international experience, statistics show that most medical negligence cases in Ireland settle. Less is known, however, about the duration of these cases, though anecdotal evidence suggests that they are protracted in nature. Procedurally focused reforms, aimed at reducing costs and facilitating more expedient resolution of these disputes have been proposed in Ireland, yet await implementation. As such, the pace of litigation is largely determined by the parties to the dispute. Drawing on the findings of an empirical study (an analysis of closed case files and qualitative interviews), this article explores two questions: first, how long do medical negligence cases take to resolve; and secondly, what contributes to delay in this context. Whilst causes of delay may vary by case, it is important to attempt to identify and explore common factors which contribute to delay. If these factors can be problematised and understood, possible solutions may be reached. In doing so, the article contributes to the debate on medical negligence reform across common law jurisdictions, evidencing the broader considerations, in addition to procedurally focused reforms, which are required when considering the issue of delay.

In this paper, we consider a general single population model with delay and patch structure, which could model the population loss during the dispersal. It is shown that the model admits a unique positive equilibrium when the dispersal rate is smaller than a critical value. The stability of the positive equilibrium and associated Hopf bifurcation are investigated when the dispersal rate is small or near the critical value. Moreover, we show the effect of network topology on Hopf bifurcation values for a delayed logistic population model.

This study presents a mathematical model that schedules arrival aircraft regarding RECAT-EU that is new categorisation for applying separation minima and analyses its effect on the performance of the Point Merge System (PMS) at Sabiha Gökcen International Airport (LTFJ). There are two main scenarios: one of them uses RECAT-EU and the other employs the ICAO wake turbulence category. Both scenarios have ten different test problems to examine the mathematical model. The model applies RECAT-EU wake turbulence categories and compares the outcome with the ICAO wake turbulence categories. The model aims to minimise flight duration on the sequencing leg and ground delay in the departure queue using the RECAT-EU and ICAO wake turbulence categories individually. The results were analysed to reveal the PMS performance using the two different approaches to turbulence categories. Statistical analysis was also carried out to compare the means of the two groups in the model.

Decision makers are often ambiguity averse, preferring options with subjectively known probabilities to options with unknown probabilities. The Ellsberg paradox is the best-known example of this phenomenon. Ambiguity has generally been studied in the domain of risky choice, and many theories of ambiguity aversion deal with ambiguity only in this context. However, ambiguity aversion may occur in other contexts. In the present experiment, we examine the effects of ambiguity in intertemporal choice. Subjects imagine they are expecting a package and must choose between two delivery options. Some delivery times are exact. Others are ambiguous, with delivery possible over a range of dates. This problem was structurally identical to the Ellsberg paradox. Subjects showed the same pattern of responses as in the traditional Ellsberg paradox, with each delivery service preferred when it was the unambiguous option. Ambiguity aversion is not specific to risk, but can also occur in other domains.

Introduction: Delays in definitive management of critically ill patients are known to drive poor clinical outcomes. A scarcely studied time period in interfacility transfer is the time between initial patient presentation and the decision to transfer. This study seeks to identify patient, environmental and institutional characteristics associated with delays in decision to transfer critically ill patients by air ambulance to a tertiary care centre. Methods: Patients >18 years old who underwent emergent air ambulance interfacility transport to a tertiary care centre were included. Patient records were located in a provincial air ambulance database. The primary exposure variable was time from patient presentation to initial call to facilitate transfer. Patient, environmental and institutional characteristics were identified using stepwise variable selection at a significance of 0.1. These characteristics were then explored using quantile regression to identify significant factors associated with delay in transport initiation. Results: A total of 11231 patients were included in the analysis. There were 5009 females (44.60%) and 6222 males (55.4%). The median age of patients was 57. The median time to initiate the transfer was 3.05 hours. The variables identified with stepwise selection were gender, category of illness, heart rate, systolic blood pressure, Glasgow coma scale, vasopressor usage, blood product usage, time of day, and type of sending site. The following factors were significantly (p < 0.05) associated with an increase in time to initiate transfer compared to the reference category at the 90th centile of time: cardiac illness (+1.45h), gastrointestinal illness (+3.27h), respiratory illness (+4.90h), sepsis (+3.03h), vasopressors (+2.31h), and an evening hour of transport (+3.67h). The following factors were significantly (p < 0.05) associated with a decrease in time to initiate transfer compared to the reference category at the 90th centile of time: neurologic illness (-1.45h), obstetrical illness (-1.56h), trauma (-3.14h), GCS <8 (-0.98h), blood transfusion (-1.47h), and sending site being a community hospital >100 beds (-2.26h), <100 beds (-4.71h), or nursing station (-10.02h). Conclusion: Time to initiate transfer represents a significant window in a patient's transport journey. In looking at the predictors of early or late initiation of transfers, these findings provide education and quality improvement opportunities in decreasing time to definitive care in critically ill populations.

The effect of signals on stability, stable throughput region, and delay in a two-user slotted ALOHA-based random-access system with collisions is considered. This work gives rise to the development of random access G-networks, which can model security attacks, expiration of deadlines, or other malfunctions, and introduce load balancing among highly interacting queues. The users are equipped with infinite capacity buffers accepting external bursty arrivals. We consider both negative and triggering signals. Negative signals delete a packet from a user queue, while triggering signals cause the instantaneous transfer of packets among user queues. We obtain the exact stability region, and show that the stable throughput region is a subset of it. Moreover, we perform a compact mathematical analysis to obtain exact expressions for the queueing delay by solving a non-homogeneous Riemann boundary value problem. A computationally efficient way to obtain explicit bounds for the expected number of buffered packets at user queues is also presented. The theoretical findings are numerically evaluated and insights regarding the system performance are derived.

Introduction: Vast geography and low population density limit availability of specialized trauma and medical care in many areas of Ontario. As such, patients with severe illnesses often require a higher level of care than local facilities can provide and thus require an interfacility transfer to access tertiary or quaternary care. In Ontario, Ornge, a provincially run air ambulance, serves as the sole provider of air-based medical and critical care transport. Patient outcomes are impacted by the time to definitive care, yet little research about reasons for delay in interfacility transfer within Ontario has been conducted. This study aimed to identify causes of delay in interfacility transport by air ambulance in Ontario. Methods: Causes of delay were identified by manual chart review of electronic patient care records (ePCR). All emergent adult interfacility transfers for patients transported by Ornge between Jan. 1-Dec. 31, 2016 were eligible for inclusion. Patient records were flagged to be manually reviewed if they met one or more of the following criteria: 1) contained a standardized delay code; 2) the ePCR free text contained “delay”, “wait”, “duty-out”, or common misspellings therein; 3) were above the 75th percentile in total transport time; or 4) were above the 90th percentile in time to patient bedside, time spent at the sending hospital, or time to receiving facility. Each trip was categorized as having delays that fall into one or more of the following categories: time-to-sending delays, in-hospital delays, and time-to-receiving/handover delays. Results: Our search strategy identified 1,220 records for manual review and a total of 872 delays were identified. The most common delays cited included aircraft refuelling (234 delays); waiting for land EMS escort (144); and unstable patients requiring advanced care such as intubation, procedures, or transfusion (79). Other delays included handover or delays at the receiving facility (42); mechanical issues (36); dispatch-related issues (53); environmental hazards (43); staffing issues (47); and equipment problems (38). Conclusion: Some common causes of interfacility delay are potentially modifiable: better trip planning around refueling, and improved coordination with local EMS could impact many delayed interfacility trips in Ontario. Our analysis was limited by number and completeness of available records, and documentation quality. To better understand causes for delay, we would benefit from improved documentation and record availability.