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Imperfect information and learning are introduced into a production-based asset pricing model. Our model features slow learning about firms’ exposure to aggregate productivity shocks over time. In contrast to a full information case, our framework provides a unified explanation for the stylized empirical features of the cross-section of stocks that differ in capital age: old capital firms (1) have higher capital allocation efficiency; (2) are more exposed to aggregate productivity shocks and, hence, earn higher expected returns, which we refer to as capital age premium; and (3) have shorter cash-flow duration, when compared with young capital firms.

Consumers gain information about the evolving value of a product both prior to purchase and when owning a product. We consider a model where both these types of gaining information are possible. The information gained when owning the product may affect future product purchases. We characterize when the consumer chooses to purchase the product if the consumer does not own it, the expected interval of time between purchases, and the expected number of product purchases over time. We find that, keeping product duration fixed, the optimal fixed price is independent of the initial product valuation if that valuation is sufficiently low such that a consumer not owning the product does not purchase it immediately, and characterize how the price charged affects the consumer information gathering strategy. When the firm can also choose product duration and there are no costs of production, we find that the firm chooses an expected production duration that is infinitely small, and charges a flow price for the consumer to use the product. We also characterize how the extent of learning when owning and when not owning the product, the duration of the product, and the discount rate affect the optimal consumer and firm strategies.

Service life is often used by state transportation agencies to evaluate alternative flexible pavement designs. The design that keeps the pavement in acceptable condition longest before it needs another maintenance treatment is the preferred design. For a given rehabilitation design, this study evaluates the pavement service life using AASHTO 1972 pavement design guide and mechanistic-empirical (M-E) pavement design guide. The service life here is referred as the duration from last completed rehabilitation to the next maintenance treatment. The goal is not to determine which method is better, but rather, it is to understand how their estimated service lives differ and what factors contribute to their differences. To perform the evaluation, rehabilitation designs developed by the SCDOT for a research project are used. These designs take into account existing pavement distresses obtained through field investigation of 28 actual rehabilitation projects on primary routes. Service life is determined using MAP-21 criteria for good and fair pavements. Analysis results indicated that 1) the predicted service life using the M-E method has lower variation than the AASHTO 1972 method and is affected by the location (rural vs. urban) of the routes, 2) the predicted service life using the AASHTO 1972 method is generally lower than that of the M-E method and is affected by the difference between the design structural number (SN) and required SN, 3) the M-E service life for good pavement is generally longer for routes that include CMRB design and the percentage of trucks lower than 10%, and 4) the AASHTO 1972 service life is higher if the existing SN is already exceeding the required SN from the previous maintenance.

In this paper Telematics and employments of Telematics in vehicles is presented. Advancement is developing every year and is used by planners of the structure in various edges for the duration of regular day to day existence. Telematics system is making in different perspectives. Apparently it gives a lot of organizations in human lives. It makes life snappier and lessening setbacks in vehicle. Telematics data gives the reason to perceiving how far, how rapidly and underneath what conditions an individual drives, similarly as a foundation for logically propelled data showing improvement. This limit is developing new and innovative things that even more correctly esteem peril and attract beneficial new customers. By then, it explains how this structure works. It is like manner tends to the favored viewpoint and block of the system. This paper in like manner fuses the issues related to the telematics structure. Finally, it will see logical examination and wrap up this paper.

The growth in Battery Powered Harbor Vessels (BPHV) signifies the necessity for a study on charging infrastructure and energy management at the seaport. This paper analyzes the charging portfolio specifications and energy management for different types of prospective BPHV at Visakhapatnam port. A brief study was conducted to identify the suitable charging method for regularly operated BPHV like Mooring Launch (ML), Pilot Launch (PL), Fishing Vessel (FV), and Tug Boats (TB). An iterative analytical study is characterized to forecast the BPHV charging load demand over a 24-hour duration and performed optimal sizing of Shore Battery Bank (SBB) to shave the critical load peaks and limit load fluctuations at the port grid. The energy management at the prospective seaport charging infrastructure is presented with relevant results by discussing the significance of Active Line Conditioner (ALC) and Bidirectional DC-DC Converter (BDC).

Access to resources by users may need to be granted only upon certain conditions and contexts, perhaps particularly in cyber-physical settings. Unfortunately, creating and modifying context-sensitive access control solutions in dynamic environments creates ongoing challenges to manage the authorization contexts. This paper proposes RASA, a context-sensitive access authorization approach and mechanism leveraging unsupervised machine learning to automatically infer risk-based authorization decision boundaries. We explore RASA in a healthcare usage environment, wherein cyber and physical conditions create context-specific risks for protecting private health information. The risk levels are associated with access control decisions recommended by a security policy. A coupling method is introduced to track coexistence of the objects within context using frequency and duration of coexistence, and these are clustered to reveal sets of actions with common risk levels; these are used to create authorization decision boundaries. In addition, we propose a method for assessing the risk level and labelling the clusters with respect to their corresponding risk levels. We evaluate the promise of RASA-generated policies against a heuristic rule-based policy. By employing three different coupling features (frequency-based, duration-based, and combined features), the decisions of the unsupervised method and that of the policy are more than 99% consistent., ACM Digital Threats: Research and Practice, 2021 30 pages, 14 Figures, 14 Tables

Given the high uncertainty in R&D work, a key concern of R&D managers is to understand how to fail faster by managing time and cost efficiently among their R&D projects. This study investigates how using a variety of Six Sigma tools can differentially affect R&D project time and cost performance. Our data are collected from 35 R&D projects in an industrial R&D center, and a total of 30 different Six Sigma tools were found to be used in varying frequencies. We analyze this problem in two phases. First, by analyzing the frequencies of tool use in the 35 projects through factor analysis, we find the application of Six Sigma tools can be guided by two distinct patterns of tool use: design-oriented tools (DT) and non-design-oriented tools (non-DT). DT include quality function deployment and failure modes and effects analysis (FMEA) for defining and understanding of the focal problems, while non-DT include Pareto chart and design of experiment for generating and testing solutions for the defined problems. Second, based on the two categories of tool use, we further analyze the effects of the two patterns of tool use on project duration and project cost. Results show that using a variety of DT is associated with faster project completion and non-DT lower project cost. In addition, the use of Six Sigma tools (both DT and non-DT) is associated with shorter durations and higher costs for complex projects but lower costs and longer durations for less complex projects. That is, different modes of trade-ff between time and cost performance can occur when project complexity is considered as a moderating factor. Overall, our findings demonstrate new insights into how using a variety of problem-solving tools affects R&D project cost and duration simultaneously.

Understanding the attitude of elderly patients is important for successful complete denture therapy. However, data regarding the impact of sociodemographic factors, impact of medical comorbidities, and duration of edentulism on the mental attitudes of such patients are lacking.The purpose of this clinical study was to explore the association of sociodemographic factors, duration of edentulism, and medical comorbidities on the attitudes of completely edentulous patients.The study was conducted among 125 completely edentulous patients visiting the Department of Prosthodontics, Nepal Medical College from August 2020 to October 2020. General information including age, sex, education level, medical history, and duration of edentulism were collected during a routine clinical evaluation. The mental attitudes of the participants were categorized according to M.M. House Classification into philosophical mind, exacting mind, hysterical mind, or indifferent mind. The participants were categorized into 3 age groups: 45 to 54 years, 55 to 64 years, and 65 years and above. Similarly, they were divided into 3 categories depending upon the duration of edentulism: 0 to 2 years, 2 to 5 years, and more than 5 years. Data were entered in a statistical software program for descriptive analysis using frequency distributions, and the chi-square statistical test and the Freeman-Halton extension of the Fisher exact test were used to determine the association between categorical variables.The highest number of participants was in the philosophical category (34.4%) with the fewest in the hysterical group (12%). Men had more philosophical and indifferent attitudes, whereas women were more exacting (P.001). Those with a recent history of extraction had an increased exacting attitude (P.001). The respondents without any comorbid diseases were more philosophical, whereas a higher percentage of respondents with different comorbid conditions were assessed to be in the hysterical category (P.001). Significant relationships were not found between mental attitude and educational status or age (P.05).Socioeconomic factors, duration of edentulism, and existing comorbidities had a significant impact on the attitudes of edentulous patients. The role of these factors should be assessed during the appraisal of the mental attitudes of edentulous patients.

Background This review summarizes applications of organizational theory and management research to cardiac surgery as it relates to patient outcomes and the adoption of new technology. Methods A total of 17 papers published in top organizational theory and management journals from 2000-2020 that examined the cardiac surgery care setting were included. Findings were classified according to two major outcomes of interest: patient care and new technology adoption patterns. Findings were further stratified based on whether predictors of these outcomes were individual-, team-, or organizational-level factors. Results A growing number of studies in the organizational theory and management literature has been using the cardiovascular care setting as a research context. Applying the various theoretical lens of organizational theory, these studies have studied how individual-, team-, and organizational-level factors influence 1) patient care outcomes such as patient mortality rates, readmission rates, post-surgery complication rates, surgery duration, and length-of-stay and 2) the adoption of new technologies or the abandonment of old technologies. Examples of these factors include task specialization, multi-siting, attribution, team familiarity dispersion, distribution of failure, workload, responsibility complementarity, expertise, team learning processes, technology status, organizational missions, and organizational status. Conclusions Well-established and studied principles from the fields of organizational theory and management research can provide unique and valuable insights into how care processes, individual attributes, systems-related factors, and the interplay between such factors affect cardiac surgical patient outcomes and clinical care. Expanding collaboration between these fields and clinicians in cardiac surgery seems prudent.

In this research, using a capability perspective, we first define innovation based, strategic flexibility (ISF) as consisting of three lower-level capabilities- market sensing,resource reconfiguration, and proactiveness; then link it to new-product/innovation outcomes, and develop a new metric of ISF that is input-output based. This metric captures a firm’s realized flexibility in translating market opportunities into innovation output. We then examine the role of CEO ties with marketing and R&D in driving a firm's ISF. Combining survey data of 191 CEOs with objective performance data, we find that (a) ISF is positively influenced by the frequency and duration of the CEO's relationship with the two functional units, but negatively influenced by CEO's relationship closeness, and (b) ISF contributes to future profit growth of firms. We test the robustness of our findings to the potential violation of sequential-ignorability assumption as defined in the causal mediation literature and find the results to be robust. The results provide insights into the role of CEO characteristics in driving innovation, and upper echelon’s contributions to marketing.

That there is an imbalance of electricity demand and supply in Ethiopia is known. Development of the Micro hydro power (MHP) system is an important technology to solve the problem. The main objective of this study was to do modelling and simulation of a micro-hydropower system for rural electrification in the case of Temecha River, Ethiopia. Yearly flow data were collected from Ethiopian Basins Development Authority and used to estimate the design flow rate. Next, modelling and simulation were done by using MATLAB SIMULINK. Some of the SIMULINK results were power and flow duration curves, and others. The nethead and design flow rates were found to be16.34m and 0.5731m3/s, respectively. Based on the preliminary analysis, the turbine selected for the site is a Kaplan turbine. It is found that, the power output of this system is greater than the electricity demand of the selected site for 346 days of the year. Thus, the systemic found to be efficient in terms of generated electrical power as compared to similar works reported in the literature. The scale of the design can be re modified to be implemented in other remote are as having river resources. Key words: electrical power, flow rate, Kaplan turbine, MATLAB SIMULINK, MHP, simulation, Zede Journal of Ethiopian Engineers and Architects, Vol. 41 (2023): Zede Journal of Ethiopian Engineers and Architects

The COVID-19 pandemic has led to the unprecedented challenge of devising massive vaccination rollouts, toward slowing down and eventually extinguishing the diffusion of the virus. The two-dose vaccination procedure, speed requirements, and the scarcity of doses, suitable spaces, and personnel, make the optimal design of such rollouts a complex problem. Mathematical modeling, which has already proved to be determinant in the early phases of the pandemic, can again be a powerful tool to assist public health authorities in optimally planning the vaccination rollout. Here, we propose a novel epidemic model tailored to COVID-19, which includes the effect of nonpharmaceutical interventions and a concurrent two-dose vaccination campaign. Then, we leverage nonlinear model predictive control to devise optimal scheduling of first and second doses, accounting both for the healthcare needs and for the socio-economic costs associated with the epidemics. We calibrate our model to the 2021 COVID-19 vaccination campaign in Italy. Specifically, once identified the epidemic parameters from officially reported data, we numerically assess the effectiveness of the obtained optimal vaccination rollouts for the two most used vaccines. Determining the optimal vaccination strategy is nontrivial, as it depends on the efficacy and duration of the first-dose partial immunization, whereby the prioritization of first doses and the delay of second doses may be effective for vaccines with sufficiently strong first-dose immunization. Our model and optimization approach provide a flexible tool that can be adopted to help devise the current COVID-19 vaccination campaign, and increase preparedness for future epidemics.

PurposeThe study explores the role of dialogic public communication and information quality (IQ) in evaluating the operational performance of donation-based crowdfunding (DBC) tasks. These tasks are primarily used to support disaster relief operations. The authors also test the influence of cognitive trust and swift trust as moderating variables in explaining the relationship between both IQ and dialogic communication with operational performance.Design/methodology/approachThe authors used a primary survey to test the hypotheses. A total of 203 responses were collected from multiple crowdfunding platforms. The authors used archival data from task creators on donation-based crowdfunding platforms, and a structured questionnaire is also used to collect responses. Data are analyzed using Warp PLS 6.0. Warp PLS 6.0 works on the principle of partial least square (PLS) structured equation modeling (SEM) and has been used widely to test path analytical models.FindingsThe authors found out that the operational performance is explained significantly by the quality of information and its association with dialogic public communication. The results support the arguments offered by dialogic public communication theory and trust transfer theory in assessing the operational success of DBC. The study also confirms that cognitive trust positively moderates the relationship between IQ and organizational public dialogic communication and operational performance. It is also revealed that the duration of the DBC task has no significant control over dialogic public communication.Practical implicationsThe study lays practical foundations for task creators on DBC platforms and website designers as it sets the importance of both IQ and dialogic communication channels. The communication made by the task creator and/or the DBC platforms with the donors and potential donors in the form of timely and appropriate information forms the key to the success of any DBC task. The study also helps task creators choose a suitable platform to improve performance.Originality/valueThe authors propose a unique framework by integrating two theoretical perspectives: dialogic public relation theory and trust transfer theory in understanding the operational performance of donation-based crowdfunding tasks. The authors address DBC tasks catering to disaster relief operations by collecting responses from task creators on DBC platforms. The study uniquely positions itself in the area of information and communication.

Over the last four decades, banking crises around the globe have become longer. This, along with the unprecedented government responses to the Great Recession of 2007/08, has led to a critical question of whether political decisions were somehow to blame for these more prolonged crises. Despite growing concerns, little attention has been given to the political and institutional determinants of financial crisis duration. Using an extensive database with 125 countries observed over the period 1976-2017 and employing a discrete-time duration model, we find that the electoral cycle, political ideology, majority governments, and institutional quality matter for the understanding of the length of financial crises. This study also shows that, to better understand the dynamics of the duration of financial crises, it is essential to look at the duration dynamics in each type of financial crisis. Finally, allowing for more flexible duration dependence patterns, we observe that the duration of banking and twin/triple crises presents a non-monotonic cubic behavior, while the probability of debt crisis ending decreases monotonically over time.

Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but existing methods for mobility prediction often overlook the underlying activity patterns. Knowledge of activity patterns can improve the performance and interpretability of existing individual mobility models, leading to more informed policy design and better user experience in intelligent transportation systems. This study develops an activity-based modeling framework for individual mobility prediction in mass transit systems. Specifically, an input-output hidden Markov model (IOHMM) approach is proposed to simultaneously predict the (continuous) time and (discrete) location of an individual's next trip using transit smart card data. The prediction task can be transformed into predicting the hidden activity duration and end location. Based on a case study of Hong Kong's metro system, we show that the proposed model can achieve similar prediction performance as the state-of-the-art long short-term memory (LSTM) model. Unlike LSTM, the proposed IOHMM approach can also be used to analyze hidden activity patterns, which provides meaningful behavioral interpretation for why an individual makes a certain trip. Therefore, the activity-based prediction framework offers a way to preserve the predictive power of advanced machine learning methods while enhancing our ability to generate insightful behavioral explanations, which is useful for user-centric policy design and intelligent transportation applications such as personalized traveler information.

This paper introduces the multiphase course timetabling problem and presents mathematical formulations and effective solution algorithms to solve it in a real case study. Consider a pool of lessons and a number of students who are required to take a subset of these lessons to graduate. Each lesson consists of a predetermined and consecutive sequence of phases with possibly different resource requirements and duration. Students who take a lesson must be present in certain phases of the lesson. Similarly, resources that are allocated to the lesson must be present in certain phases of the lesson depending on their roles. The objective is to maximize the weighted sum of student-lesson allocations subject to some capacity, availability and prerequisite constraints. The problem is formulated as an integer linear program called the session-index formulation. The formulation is then extended by introducing various side constraints that capture business requirements of a pilot training program. An enhanced branch-and-check algorithm is proposed to solve the problem more efficiently. Large instances of the problem are solved by utilizing an effective fix-and-optimize matheuristic. Results of a computational study on a set of real-world instances of the problem demonstrate the efficacy of the proposed exact and matheuristic algorithms. Due to the novelty and complexity of the problem, the efficacy of the proposed methodology to solve its real-world instances, and the implementation and realization of these contributions within a decision support system, this work was recognized as a semi-finalist of INFORMS Franz Edelman Award (2021).

With the widespread application of mobile phones, it has become possible to study human mobility and travel behaviors based on cellular network data. Contrary to call detail records, the data is triggered by mobile cellular signaling and can provide fine-grained information about users' daily routines. However, it does not explicitly provide semantic details about traveling traces, e.g., trip purposes. In this paper, we propose a methodological framework to handle large-scale cellular network data and discover the underlying trip purposes in an unsupervised way. We first devise heuristic rules to identify home/work purposes. Then, a flexible latent Dirichlet allocation (LDA) model is presented to discover the activities for remaining trips, in which each trip is depicted by four attributes, i.e. arrival time, age group, stay duration, and the point of interest tag for the destination. Experimental results show that the proposed method can identify diverse trip purposes by explaining their structures over trip attributes and outperform baselines in terms of log-likelihood and perplexity. We also analyze the difference between the automatically discovered trip purposes and those estimated from household census, and the analyzed results demonstrate the feasibility of our proposed method.

Accurate destination prediction over sub-trajectories is essential for a wide range of location-based services. Traditional trip matching methods fail to capture temporal dependence hidden in trajectories and may suffer from data sparsity problems. With the help of massive trajectory data, state-of-the-art approaches based on deep learning (DL) have achieved great success. However, existing DL approaches rarely consider the influence of individual travel preferences in destination prediction. When the trip is long but the known partial trajectory is short, DL models are unable to produce satisfactory results. Thus, we design a feature extraction mechanism to extract useful temporal features, spatial features, and static covariates for destination prediction, among which the spatial features characterize individual travel preferences by considering two main movement patterns in daily travel. Then, a hierarchical model including multiple modules is proposed to finely process heterogeneous features. Extensive experiments conducted on two public datasets demonstrate the superior performance of the proposed model compared to the state-of-the-art methods. Moreover, further experimental results show that the proposed model still performs well when trajectory prefix is short or travel duration is long, which confirms the effectiveness of integrating individual travel preferences.

In the era of Connected and Autonomous Vehicles, platooning has the potential to increase roadway capacity and reduce energy consumption. However, as vehicles try to form platoons they may also expend extra energy. Also, depending on its position within a platoon, the energy savings of each vehicle can be different. Thus, optimizing and quantifying the savings that may be gained from platooning is challenging. In this paper, we develop a simulation-optimization framework to tackle this challenge of quantifying energy savings from platooning. Our optimization model determines vehicle-to-platoon assignments given the current location, speed, and destination of all the vehicles and platoons on the freeway. The simulation model takes these platooning decisions from the optimization model and implements them. Vissim is used to simulate the actions taken by all the vehicles and platoons and capture the energy expended by each vehicle over its entire trip duration. To quantify the energy savings, the system is simulated with and without platooning. That is, the optimization model is turned off when assessing the performance of the system without platooning. In addition to the simulation-optimization framework, an accurate energy consumption model is developed in this paper, which is inspired by the work of Tadakuma and colleagues. The energy consumption model utilizes a hybrid prediction formula for aerodynamic drag reduction in multi-vehicle formations unifying both physical mechanisms and existing empirical study data. Our results show that a system wide savings of about 3% can be realized over 160 kilometers when platoons are formed strategically.

Video conference meetings, which became frequent during the COVID‐19 pandemic, might result in exhaustion (so‐called “Zoom fatigue”). However, only little is known about “Zoom fatigue,” the objective characteristics shaping it, and the subjective experiences eliciting this phenomenon. Gaining this knowledge is critical for understanding work life during the pandemic. Study 1, a within‐person quantitative investigation, tested whether video conferences are exhausting and if objective characteristics (i.e. meeting size, meeting duration, and the presence of the supervisor) moderate “Zoom fatigue”. Employees from Germany and Israel (N = 81) participated in a 2‐week study, with meetings nested within persons (n = 988). Results showed that video conferences are exhausting—more than meetings held through other media. However, objective characteristics did not moderate this relationship. In Study 2, qualitative data from Germany and Israel (N = 53) revealed employees' subjective experiences in video conferences that may lead to “Zoom fatigue”. These include, for example, experiences of loss and comparison with the “good old times” before the pandemic. Employees suggested ways to mitigate “Zoom fatigue,” particularly, better management of meetings by leaders. Our results provide empirical support for “Zoom fatigue” and suggest which subjective experiences elicit this phenomenon, opening directions for research and practice. [ FROM AUTHOR] Copyright of Applied Psychology: An International Review is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all Abstracts.)

Recurrent neural networks (RNNs) can be used to operate over sequences of vectors and have been successfully applied to a variety of problems. However, it is hard to use RNNs to model the variable dwell time of the hidden state underlying an input sequence. In this article, we interpret the typical RNNs, including original RNN, standard long short-term memory (LSTM), peephole LSTM, projected LSTM, and gated recurrent unit (GRU), using a slightly extended hidden Markov model (HMM). Based on this interpretation, we are motivated to propose a novel RNN, called explicit duration recurrent network (EDRN), analog to a hidden semi-Markov model (HSMM). It has a better performance than conventional LSTMs and can explicitly model any duration distribution function of the hidden state. The model parameters become interpretable and can be used to infer many other quantities that the conventional RNNs cannot obtain. Therefore, EDRN is expected to extend and enrich the applications of RNNs. The interpretation also suggests that the conventional RNNs, including LSTM and GRU, can be made small modifications to improve their performance without increasing the parameters of the networks.

Real-time parking availability prediction is of great value to optimize the on-street parking resource utilization and improve traffic conditions, while the expensive costs of the existing parking availability sensing systems have limited their large-scale applications in more cities and areas. This paper presents the MePark system to predict real-time citywide on-street parking availability at fine-grained temporal level based on the readily accessible parking meter transactions data and other context data, together with the parking events data reported from a limited number of specially deployed sensors. We design an iterative mechanism to effectively integrate the aggregated inflow prediction and individual parking duration prediction for adequately exploiting the transactions data. Meanwhile, we extract discriminative features from the multi-source data, and combine the multiple-graph convolutional neural network (MGCN) and the long short-term memory (LSTM) network for capturing complex spatio-temporal correlations. The extensive experimental results based on a four-month real-world on-street parking dataset in Shenzhen, China demonstrate the advantages of our approach over various baselines.

In intermodal transportation, it is essential to balance the trade-off between the cost and duration of a route. The duration of a path is inherently stochastic because of delays and the possibility of overbooking. We study a problem faced by a company that supports shippers with advice for the route selection. The challenge is to find Pareto-optimal solutions regarding the route's costs and the probability of arriving before a specific deadline. We show how this probability can be calculated in a network with scheduled departure times and the possibility of overbookings. To solve this problem, we give an optimal algorithm, but as its running time becomes too long for larger networks, we also develop a heuristic. The idea of this heuristic is to replace the stochastic variables by deterministic risk measures and solve the resulting deterministic optimization problem. The heuristic produces, in a fraction of the optimal algorithm's running time, solutions of which the costs are only a few percent higher than the optimal costs.

During the summer of 2020, a survey-based study was conducted at North Carolina State University, a land-grant university, to evaluate the impact of COVID-19 on pre-veterinary students’ ability to gain experience hours for Doctor of Veterinary Medicine (DVM) admissions. Of the 286 respondents (47% of the respondent pool), 92% reported losing at least one animal, veterinary, research, extracurricular, or work opportunity due to COVID-19, and 59% were not able to find a replacement. Of the lost experiences, 74 (20.8%) were for academic credit, resulting in 131 total academic credit hours lost, while only 12 credit hours were gained via alternative experiences. Of respondents, 30% (29.7%) identified as applicants of the 2020–2021 Veterinary Medical College Application Service (VMCAS) cycle. More than half (52.6%) of the sample identified being concerned about the strength of their VMCAS experiences due to these lost opportunities. Many respondents reported considering delaying application submissions by taking a gap year (17.5%) or having had their intended graduation timeline affected (14.8%). Since the majority of veterinary colleges utilize a holistic review process, this study provides a basis for understanding the effects of COVID-19 on the duration, depth, and diversity of experiences gained by future DVM applicants. This article also provides recommendations for DVM admissions adaptations based on the outcomes of the data.

Finding a parking space is a daunting task that frustrates drivers, affects the economic efficiency of carriers and impacts city sustainability. Between meeting the needs of deliverers (location, accessibility, proximity...) and individuals (price, duration, closeness...), transport and urban traffic planners find themselves in a conflict of interest. Hence the importance of a tool that manages the entire urban parking system in real time. This paper presents, in this perspective, a solution that allocates parking spaces to carriers and individuals under uncertain conditions. Its principle is founded on two allocation levels. The first distributes parking requests on the city areas by considering three characteristic indicators. The second ranks, for each driver, the available spaces in the designated area according to their decreasing non-dominated degree. It additionally includes a conflict management approach for dealing with the assignment of multiple drivers to the same place. It is deployed and its performance is tested by administering parking in a city with four urban areas.

Introduction Research studies have repeatedly shown that better educated nurses provide safer and higher quality care. In Rwanda, neonatal nursing is not recognized as a nursing specialty and there is no consistent approach in neonatal education or training of nurses who work with newborn infants. Purpose The purpose of the study was to describe existing neonatal nursing practices in Rwanda, specifically 1) orientation program content and orientation process; 2) nurses' education, skill levels and comfort with performing neonatal care; and 3) availability and use of medical equipment. Methods A descriptive cross-sectional survey design was used. Data were collected from October through November 2017. A convenience sample of nurses and midwives was used; nurses and midwifes working in Rwandan neonatal care units (NCUs) were invited to participate and complete a questionnaire specifically designed for this study. Results A total of 99 participants representing all 47 Neonatal Care Units (including 2 neonatal Intensive care units-NICUs) in Rwanda completed the questionnaire. Seventy-six (76) were nurses, 19 were midwives, and 4 were nurse-midwives. A lack of consistency in orientation/training to the neonatal unit was evident with approximately 70% who felt that the orientation was not sufficient. Comfort level with skills depended on the type of procedure. Basic neonatal care procedures were reported as comfortable, however this level varied across the geographic regions. Some vital equipment was unavailable. For more than half the respondents there was no 24-h physician coverage available for back up. Discussion The findings of this exploratory descriptive study reveal that the majority of Rwandan nurses/midwifes had limited neonatal care experience. Orientation program content and duration varied across the country. Most included basic content but was not always sufficient when there was poor staffing or a lack of physician coverage. Procedures that were rarely performed were uncomfortable because there was no opportunity for practice. Competency demonstration not expected of most of the respondents. Conclusions There needs to be consistent education/training for neonatal nurses. Competency based education and practice performance should be developed. Further research needs to focus on staffing patterns, education/competencies of the nursing staff, and the direct impact on neonatal outcomes.

Objectives To conduct a nationwide evaluation of student and program director (PD) perspectives of virtual subinternships that took place during the COVID-19 pandemic. Methods In December 2020, we distributed anonymous surveys to all 534 urology residency applicants and 161 urology PDs at academic medical centers across the United States to evaluate virtual subinternships. Surveys assessed curriculum composition, goals, satisfaction, barriers, and future reusability. The primary outcome was overall satisfaction with the subinternship, evaluated on a 5-point Likert scale. Results The survey was completed by 174 students (33%) and 82 PDs (51%), including 81 students (47%) and 32 PDs (41%) who participated in virtual subinternships at 29 institutions. Overall, 77% of students and 78% of PDs rated the electives "excellent" or "very good." On ordinal logistic regression, higher student ratings were associated with duration ≥3 weeks (OR 4.64, p=0.003) and class size ≤4 students (OR 3.33, p=0.015). Higher PD ratings were associated with full-time electives (OR 11.18, p=0.019), class size ≤4 students (OR 13.99, p=0.042), and utilization of the standardized Guidebook from the Society of Academic Urologists (OR 11.89, p=0.038). The most commonly reported challenge to the subinternship's efficacy was lack of hands-on learning (87% of students and 81% of PDs). Looking forward, 45% of students and 66% of PDs recommended incorporating virtual components into future electives. Conclusions The virtual subinternship was a coordinated nationwide initiative to provide urologic education to medical students during a pandemic. The most successful courses were structured with longer duration, full-time commitment, and small class size.

Container drayage operations with tractor-trailer distinction and trailer reposition exist widely in real-world logistics yet have seldom been reported in the literature. This paper studies a container drayage problem with trailer reposition (CDPTR), in which trailers might be provided at pickup locations and kept at delivery locations. A two-stage solution framework is adopted to solve the problem. The first stage generates tractor schedules to maximize profit, and the second stage aims to ensure sufficient trailers at each trailer depot during the container drayage operation. A hybrid large neighborhood search and tabu search (LNS-TS) heuristic is developed to solve the problem. We construct 72 instances with different characteristics, for example, time window duration, initial trailer resource and distribution, and the number of requests. Results show that our solutions outperform the solutions obtained from the solver. A detailed analysis of the results also reveals several insights for container drayage operators.

A write transaction intending to access data already accessed by the group of transactions in read mode may starve. The situation may even become worse with the simultaneous occurrence of the priority inversion if the priority level of the requesting write transaction is higher than that of the highest priority lock holding read transaction from the group of transactions currently holding the conflicting data in read mode. A Reads-write Avoid starvation and Priority Inversion period Declined (RAPID) commit protocol has been proposed that efficiently resolves the above problem by declining the priority inversion duration optimistically through lessening the number of reader transactions either requested or currently holding the conflicting data item in read mode by aborting some of them (if possible). The simulation results show that the RAPID outperforms 2PC and PIC protocol.

By combining independent systems (e.g., financial systems, power infrastructure, transportation networks), systems of systems (SoSs) can provide additional services. The ability to withstand faults, minimize service disruption, and efficiently recover (resilience) is a key performance characteristic. Current resilience metrics, however, require improvement. Each SoSs is approached with different assumptions about fault magnitude and recovery strategy. Thus, engineers currently have difficulty answering the basic question: Between alternative designs A and B, which is more resilient? SoS resilience metrics should reflect that the system interfaces are the most potent leverage point, have a standardized fault and recovery strategy, incorporate SoS dynamics, and apply to both natural and artificial systems of systems. In response, we develop the system of systems resilience metric (SoSRM). SoSRM methodology uses the definition of SoS to identify appropriate fault locations and graph theory to define fault and recovery duration. SoSRM is applied to two case studies to demonstrate application. First, an intertidal oyster reef ecosystem's system dynamics model is presented to demonstrate SoSRM methodology. Next, a military SoS demonstrates the application of SoSRM to a stochastic multi-agent system. Validation simulations show that SoSRM reflects expected fault impact and that methodological choices for fault duration do not drive SoSRM results. This article presents a new design tool capable of comparing the resilience between two SoS architectures.

The 2015 Pan American and Parapan American Games will be hosted in Toronto. The Athletes’ Village is being constructed in the West Don Lands and post Games will be transformed into a mixed-use community. Due to the scale of the project and brief duration of the Games, the creation of this new neighbourhood in Toronto is a sizeable undertaking but has the potential to accommodate Toronto’s anticipated growth and serve to connect the Waterfront from east to west. The central issue for this paper is identifying the legacy aspects of the Athletes’ Village and how stakeholders can work to mitigate any potential negative impacts identified. The investigation will focus on the social, environmental and economic impacts. Recommendations for stakeholders are presented based on the research approach which included interviews, a review of literature as well as case studies of previous sporting events in Winnipeg, Vancouver, Calgary, Rio de Janeiro, Brazil and Guadalajara, Mexico.

This article contributes to the relationship between fiscal fraud and tax collection in the Spanish economy, creating a composite suspicion tax fraud indicator (STFI) based on Google Trends searches to study the dynamics and foresee tax revenues evolution in Spain. Also, we expand knowledge in the field of fraud tax indicators, following the UNODC (2020) and OECD (2016) recommendations. To this purpose, we apply factor analysis to create the composite indicator and next we utilize techniques centred on fractional integration (ARFIMA) and fractional cointegration VAR (FCVAR) to assess the STFI behavior against tax collection and GDP. The outcomes indicate that the d is less than 1 in all the time series analyzed. As we can see, the tax collection and the leading indicator have a similar statistical behavior (d = 0.49 and d = 0.40, respectively), which implies mean reversion. On the other hand, GDP will behave similarly to the other two time series, with d = 0.05, which implies that the shocks will have a temporary effect on the GDP behavior, and these effects will disappear by themselves in the short term and in less time than the other two time series. FCVAR results indicate a short-lived duration of the shock due to the error correction term and their short-run stationary behavior. In the end, applying wavelet analysis, we determine that the composite suspicion tax fraud indicator maintains a negative association with tax collection, except in 2017 and 2018, when the high economic growth offsets the fiscal fraud.

The operating theater is one of the most expensive units in the hospital, representing up to 40% of the total expenses. Because of its importance, the operating room scheduling problem has been addressed from many different perspectives since the early 1960s. One of the main difficulties that has reduced the applicability of the current results is the high variability in surgery duration, making schedule recommendations hard to implement. In this work, we propose a time-indexed scheduling formulation to solve the operational problem. Our main contribution is that we propose the use of chance constraints related to the surgery duration’s probability distribution for each surgeon to improve the scheduling performance. We show how to implement these chance constraints as linear ones in our time-indexed formulation, enhancing the performance of the resulting schedules significantly. Through data analysis of real historical instances, we develop specific constraints that improve the schedule, reducing the need for overtime without affecting the utilization significantly. Furthermore, these constraints give the operating room manager the possibility of balancing overtime and utilization through a tuning parameter in our formulation. Finally, through simulations and the use of real instances, we report the performance for four different metrics, showing the importance of using historical data to get the right balance between the utilization and overtime.

The COVID-19 pandemic that erupted in 2020 forced businesses across the world to adopt virtual meetings. With many people working from home, software platforms like Zoom and Teams became ubiquitous, but their widespread use also revealed many weaknesses and limitations. While technologies for virtual meetings have existed for decades, these technologies have advanced significantly in recent years, and today range from audioconference facilities to telepresence rooms with high-resolution video and sophisticated virtual presence features. The available alternatives differ significantly in costs, complexity and capabilities, and choosing the most effective technology for each meeting setting is not always easy. This is important, since after the pandemic, virtual meetings will move from being a necessity brought on by the pandemic to being a widely accepted alternative to traditional face-to-face meetings. Consequently, the questions of when and how to meet virtually will become even more significant. In this article, we describe a decision-making framework for choosing when and how to meet virtually, based on matching the appropriate communication capabilities with various meeting objectives and taking into account meeting size and duration. The framework is based on extensive empirical research conducted in partnership with several major U.S. and European companies.

This paper presents exact and approximate methods for constructing daily walking tours for parking enforcement officers the Parking Enforcement Routing Problem (PERP), a problem faced by EMEL, a municipal company that manages parking in Lisbon. Parking Enforcement Officers (PEO) must ensure that the users of the street parking lots pay the corresponding fee, and that they also comply with the parking rules imposed by the legislation in force. The duration of the tours must be compatible with the PEO daily work schedules. A street cannot be supervised more than once in consecutive hours, nor supervised simultaneously by two PEOs. The PERP is a new generalisation of an Arc Routing Problem with profits aiming to maximise the total criticality, a concept defined to represent the need for enforcement of the street segments, related to the number of vehicles parked in transgression. Moreover, parking rules lead to some specificities regarding the underlying network, which needs to be built with additional unusual characteristics. A flow-based mixed integer linear programming model and heuristics which return two daily trips for each PEO are proposed. Computational results carried out on 50 instances based on real data show that it was appropriate to propose both types of methods, exact and heuristic. Furthermore, PEOs working for EMEL reacted positively to the trips corresponding to the generated solutions. A Decision Support System built on the heuristics was also developed to be easily used by practitioners in EMEL info:eu-repo/semantics/publishedVersion

Pharmaceutical firms typically enjoy market exclusivity for new drugs from concurrent protection of the underlying invention (through patents) and the clinical trials data submitted for market approval (through data exclusivity). Patent invalidation during drug development renders data exclusivity the sole source of protection and shifts the period of market exclusivity at the project level. Patent invalidation therefore constitutes a natural experiment that allows us to causally identify how the duration of market exclusivity affects firms' incentives to innovate. In instrumental variables regressions we quantify the effect of a one-year reduction in expected market exclusivity on the likelihood of drug commercialization. We provide first estimates of the responsiveness of R&D investments to market exclusivity expectations and hereby inform the ongoing policy debate on the regulation of intellectual property rights in the pharmaceutical industry.

Wearable activity recognition can collate the type, intensity, and duration of each child's physical activity profile, which is important for exploring underlying adolescent health mechanisms. Traditional machine-learning-based approaches require large labeled data sets; however, child activity data sets are typically small and insufficient. Thus, we proposed a transfer learning approach that adapts adult-domain data to train a high-fidelity, subject-independent model for child activity recognition. Twenty children and twenty adults wore an accelerometer wristband while performing walking, running, sitting, and rope skipping activities. Activity classification accuracy was determined via the traditional machine learning approach without transfer learning and with the proposed subject-independent transfer learning approach. Results showed that transfer learning increased classification accuracy to 91.4% as compared to 80.6% without transfer learning. These results suggest that subject-independent transfer learning can improve accuracy and potentially reduce the size of the required child data sets to enable physical activity monitoring systems to be adopted more widely, quickly, and economically for children and provide deeper insights into injury prevention and health promotion strategies.

The antecedents of firms' R&D internationalization have attracted the attention and discussion of scholars. However, the manner and circumstances in which a firm's past innovation performance affects its R&D internationalization decisions have not yet been discussed. Based on the behavioral theory of the firm (BTOF), we propose that persistent innovation underperformance might serve as a motivating factor to trigger R&D internationalization and that the top management team (TMT) determines how responsive to persistent innovation underperformance the firm is likely to be. Results from empirical analyses of panel data from 1323 listed Chinese manufacturing companies over a 9-year period indicate that a longer duration of persistent innovation underperformance increases the likelihood that firms will implement R&D internationalization. Integrating the moderating roles of multidimensional TMT human capital, we further find that TMT education level and technical background strengthen the abovementioned relationship, while TMT international experience weakens the abovementioned relationship. The contributions, implications, and limitations of this study are fully discussed.

Introduction An understanding of which trainee factors Endourology Society (ES) fellowship program directors (PDs) value may help urology residents optimize their fellowship candidacy and help current fellows optimize their fellowship performance. In the present study, we evaluated which factors are most important to ES PDs in selecting fellowship candidates and evaluating current fellows. Methods A survey was constructed using REDCap software and emailed to PDs of fellowships registered with the ES. Fellowships in the ES include Endourology-Stone Disease (ESD) programs, robotic-laparoscopic surgery programs (MIS), and combination programs. A Likert scale ranging from 1 (not important) to 5 (extremely important) was used to rank the criteria for selecting and evaluating fellows. The survey captured program demographics such as geographic region, program type, and program duration. Results Of the 52 ES PDs, 35 (67%) responded. Respondents represented 8 (23%) ESD programs, 7 (20%) MIS programs, and 20 (57%) combination programs. Furthermore, sixteen (46%) represented one-year programs and 19 (54%) represented two-year programs. The top-five factors for candidate selection were perceived fit of the applicant, perceived level of applicant interest, initial interview, personal emails from applicants' advocates, and personal phone calls from applicants' advocates. Advocacy on behalf of the applicant appeared to be especially important for two-year fellowships. The top-five factors for the evaluation of fellows in training were patient interactions, professionalism, attitude/demeanor, operative skills, and interactions with ancillary staff. Research productivity was of greater importance for the evaluation and selection of two-year fellows and ESD fellows. Conclusions Applicants for ES fellowships should focus on the initial interview and recruiting supportive mentors to advocate for their applications, particularly for two-year programs. Though PDs value both clinical skills and research productivity, research productivity was more important for two-year programs and ESD programs. Further research into applicant perspectives on the fellowship application process is warranted.

This paper studies job search behavior in the midst of a pandemic recession. We use long-running panel data from the Netherlands (LISS) and complement the core survey with our own COVID-specific module, conducted in June 2020. The survey provides data on the job search effort in terms of the number of applications of employed as well as unemployed respondents. We estimate an empirical model of job search over the business cycle over the period 2008–2019 to explore the gap between predicted and actual job search behavior in 2020. We find that job search during the pandemic recession differs strongly from previous downturns. The unemployed search significantly less than what we would normally observe during a recession of this size. For the employed, the propensity to search is even greater than what we would expect, but those who do search make significantly fewer job applications. Expectations about the duration of the pandemic seem to play a key role in explaining job search effort for the unemployed in 2020. Furthermore, employed individuals whose work situation has been affected by COVID-19 are searching more actively for a new job.

Nowadays, electric taxis (e-taxis) are gradually replacing gasoline taxis in todays public transportation systems due to their environmental-friendly nature. Unlike gasoline taxis, the charging duration of the e-taxis spent at charging stations can be as long as hours. Meanwhile, charging posts are typically sparsely and unevenly distributed across a city. Without consideration of e-taxis charging decisions, the existing relocation recommendation schemes, which are effective for gasoline taxis, become far from optimal for the e-taxis. Therefore, we design a joint charging and relocation recommendation system for e-taxi drivers (CARE). Technically, we formulate e-taxi drivers decision making as a multi-agent reinforcement learning problem where each e-taxi driver aims to maximize his cumulative rewards. Specifically, we propose a novel multi-agent mean field hierarchical reinforcement learning (MFHRL) framework. The hierarchical architecture of MFHRL helps CARE provide far-sighted charging and relocation recommendations for e-taxi drivers. Besides, we integrate each hierarchical level of MFHRL separately with the mean field approximation to incorporate e-taxis mutual influences in decision making. We conduct extensive experiments on one of the largest real-world e-taxi datasets in Shenzhen, China, and validate that the proposed MFHRL framework greatly outperforms all baselines by significantly increasing the rewards obtained by e-taxi drivers.

Commuting demands a considerable amount of time from a worker's daytime. Numerous studies have shown the relationship between commuting and mood, and even job performance. Contradictory findings are also present in the literature regarding the association between commuting, mood and job performance. Therefore, the aim of the present study is to examine the association between commuting, negative upon arrival at work and individual job performance, given the special focus on the mediating role of negative mood. Two hundred and twenty-six participants were recruited via snowball sampling. Participants were asked to answer questions about commute duration and distance, to rate their mood upon arrival at work and to fill in the individual work performance questionnaire. Results showed that commute duration and distance was associated with job performance through mood upon arrival at work. It is believed that this study provides valuable information regarding the mediating role of mood upon arrival at work.

Background Cooling thermal burns with running water is a recommended first aid intervention. However, guidance on the ideal duration of cooling remains controversial and inconsistent across organisations. Aim To perform a systematic review of the evidence for the question; Among adults and children with thermal burn, does active cooling using running water as an immediate first aid intervention for 20 minutes or more, compared with active cooling using running water for any other duration, change the outcomes of burn size, burn depth, pain, adverse outcome (hypothermia) or complications? Method We searched Medline, Embase, Cochrane Database of Systematic Reviews and used ROBINS-I to assess for risk of bias. We used Grading of Recommendations, Assessment, Development and Evaluation methodology for determining the certainty of evidence. We included all studies that compared the selected outcomes of the duration of cooling of thermal burns with water in all patient ages. (PROSPERO registration number: CRD42021180665). From 560 screened references, we included four observational studies. In these studies, 48% of burns were cooled for 20 minutes or more. We found no benefit for a duration of 20 minutes or more of cooling when compared with less than 20 minutes of cooling for the outcomes of size and depth of burn, re-epithelialization, or the need for skin grafting. The evidence is of very low certainty owing to limitations in study design, risk of bias and indirectness. Conclusion The optimal duration of cooling for thermal burns remains unknown and future prospective research is required to better define this treatment recommendation.

This article considers heterogeneous, cold standby systems performing missions with the fixed amount of work when a failure of an operating element results in a mission failure. A system is operating in a random environment modeled by the Poisson process of shocks. Each shock decreases the remaining lifetime of an operating element and, therefore, its preventive replacement (PR) is scheduled on experiencing the predetermined number of shocks. An important feature of the discussed model is that the failure can also occur during these PRs (task transfers) with two elements involved. The duration of the task transfer depends on the time from the start of a mission. The recursive equations for obtaining the mission success probability are derived and the corresponding numerical algorithm is developed. The number of shocks triggering elements' replacements is obtained as a solution of the formulated optimization problem. The numerical example with the detailed analysis for a set of virtual machines operating in a cloud computing environment is presented.

A substantial part of traffic congestion is triggered by unplanned incidents such as crashes, breakdowns and hazards, reducing road capacity and increasing the delays, pollution, and productivity losses. Previous studies on incident duration have focussed on individual incidents and the influencing factors that could be obtained directly from the incident description. Consequently, the explanatory variables were more localized, and the impacts of broader macro-level factors were not explored. This contrasts with the studies on incident frequency, where the influencing factors are typically collected at a macro-level. Therefore, this study aims to explore the impact of various factors associated with reported vehicle breakdown duration at a macro-level. Street network characteristics such as connectivity, density, and hierarchy were included as covariates, in addition to the demographic, vehicle utilization, and environmental variables. The dataset contains over 72,000 vehicle breakdowns records within 4.5 years (January 2012 to June 2016) in Greater Sydney, Australia involving 44 SA3s (Statistical Area Level 3). After a principal component dimension reduction of independent variables, a fixed-parameters accelerated failure time (AFT) hazard-based model with underlying log-logistic, log-normal and Weibull distributions were used in this analysis. Weibull hazard distribution with gamma frailty and the latent class models were also considered to account for unobserved heterogeneity. The latent class model provides the best fit where road network connectivity, hierarchy, and familiarity factors are considered to have both positive and negative impact on duration; higher road network density, mixed land-use, and spatial disorientation of roads are associated with longer duration; and higher income and exposure (vehicle kilometres travelled) are associated with shorter duration. The results will help incident management agencies to better allocate current response resources and predict the resources required in the future. Besides, the results associated with network structure measures can provide valuable insights to community planning authorities to manage unplanned congestion.

As the complexity of flight missions continues to increase, sending a timely warning or providing assistance to pilots helps to reduce the probability of operational errors and flight accidents. Monitoring pilots’ physiological data, real-time evaluation of mission load is a feasible technical way to achieve this. In this paper, a set of flight tasks including aircraft control, human-computer interaction and mental arithmetic tests are designed to simulate five mission loads at different flight difficulty levels. A sensitivity analysis method based on a comprehensive test is proposed to select a set of sensitive physiological factors. Then, based on the SVM hierarchical combination classification method, the pilot mission load real-time evaluation model is established. The test results show significant differences in EMG, respiration rate (abdomen), heart rate, blood oxygen saturation, pupil area, fixation duration, number of fixations, and saccades. The high accuracy obtained from experiments proved that the proposed real-time evaluation model is applicable to meet the requirements of real working environments. The findings can provide methodological references for mission load evaluation research in other fields.

The storm surge and hydrodynamics along the Krishna-Godavari (K-G) basin are examined based on numerical experiments designed from assessing the landfalling cyclones in Bay of Bengal (BoB) over the past 38 years with respect to its highest maximum sustained wind speed and its duration. The model is validated with the observed water levels at the tide gauge stations at Visakhapatnam during Helen (2013) and Hudhud (2014). Effect of gradual and rapid intensification of cyclones on the peak water levels and depth average currents are examined and the vulnerable locations are identified. The duration of intensification of a rapidly intensifying cyclone over the continental shelf contributed to about 10-18 % increase in the peak water levels, whereas for the gradually intensifying cyclone the effect is trivial. The inclusion of the wave-setup increased the peak water levels up to 39% compared to those without wave-setup. In the deep water region, only rapidly intensifying cyclones affected the peak MWEs. Intensification over the continental slope region significantly increases the currents along the shelf region and coast. The effect on peak maximum depth averaged current extends up to 400 km from the landfall location. Thus, it is necessary to consider the effect of various combinations of the highest cyclone intensity and duration of intensification for identifying the worst scenarios for impact assessment of coastal processes and sediment transport. The study is quite useful in improving the storm surge prediction, in preparedness, risk evaluation, and vulnerability assessment of the coastal regions in the present changing climate.

Rationale: Frailty in critically ill patients is associated with higher mortality and prolonged length of stay, however little is known about the impact on the duration of mechanical ventilation. O...

Background Neonates often receive noninvasive respiratory support via continuous positive airway pressure (CPAP) or high-flow nasal cannula oxygen (HHFNC). The decision to change from one mode to the other, however, is not evidence based, hence not standardized and does not consider cost implications. Purpose To assess the introduction of a care bundle for the medical and nursing staff in a tertiary medical and surgical neonatal center with regard to any financial savings or adverse outcomes. Methods An education package and written guidelines were used to increase the awareness of the durations for which CPAP and HHFNC Vapotherm (VT) circuits could be used and the costs of the circuits. Results This resulted in a cost saving of £17,000 ($22,254) for the year without adverse outcomes. Implications for practice Introduction of a care bundle involving an education package and written guidelines to increase the awareness of the durations that circuits could be used and the costs of CPAP and HHFNC circuits among the medical and nursing staff can lead to cost savings when incorporated into clinical practice. Implications for research Strategies, particularly during weaning, which involve changing from one form of noninvasive respiratory support to another, need a greater evidence base. Future research should include awareness of the duration different circuits could be used and the cost implications of changes between modes and hence circuits.