Time Metrics Research Articles

Five multivariate Duchenne muscular dystrophy progression models bridging six-minute walk distance and MRI relaxometry of leg muscles.

The study aimed to provide quantitative information on the utilization of MRI transverse relaxation time constant (MRI-T2) of leg muscles in DMD clinical trials by developing multivariate disease progression models of Duchenne muscular dystrophy (DMD) using 6-min walk distance (6MWD) and MRI-T2. Clinical data were collected from the prospective and longitudinal ImagingNMD study. Disease progression models were developed by a nonlinear mixed-effect modeling approach. Univariate models of 6MWD and MRI-T2 of five muscles were developed separately. Age at assessment was the time metric. Multivariate models were developed by estimating the correlation of 6MWD and MRI-T2 modelvariables. Full model estimation approach for covariate analysis and five-fold cross validation were conducted. Simulations were performed to compare the models and predict the covariate effects on the trajectories of 6MWD and MRI-T2. Sigmoid Imax and Emax models best captured the profiles of 6MWD and MRI-T2 over age. Steroid use, baseline 6MWD, and baseline MRI-T2 were significant covariates. The median age at which 6MWD is half of its maximum decrease in the five models was similar, while the median age at which MRI-T2 is half of its maximum increase varied depending on the type of muscle. The models connecting 6MWD and MRI-T2 successfully quantified how individual characteristics alter disease trajectories. The models demonstrate a plausible correlation between 6MWD and MRI-T2, supporting the use of MRI-T2. The developed models will guide drug developers in using the MRI-T2 to most efficient use in DMD clinical trials.

Semi-Mechanistic Prediction and Optimization of Residence Time Metrics of a Starve-Fed Extruder via a Hybrid Machine-Learning Convection–Diffusion Model

Semi-Mechanistic Prediction and Optimization of Residence Time Metrics of a Starve-Fed Extruder via a Hybrid Machine-Learning Convection–Diffusion Model

Multi-robot navigation based on velocity obstacle prediction in dynamic crowded environments

PurposeThe purpose of this paper is to propose a new velocity prediction navigation algorithm to develop a conflict-free path for robots in dynamic crowded environments. The algorithm BP-prediction and reciprocal velocity obstacle (PRVO) combines the BP neural network for velocity PRVO to accomplish dynamic collision avoidance.Design/methodology/approachThis presented method exhibits innovation by anticipating ahead velocities using BP neural networks to reconstruct the velocity obstacle region; determining the optimized velocity corresponding to the robot’s scalable radius range from the error generated by the non-holonomic robot tracking the desired trajectory; and considering acceleration constraints, determining the set of multi-step reachable velocities of non-holonomic robot in the space of velocity variations.FindingsThe method is validated using three commonly used metrics of collision rate, travel time and average distance in a comparison between simulation experiments including multiple differential drive robots and physical experiments using the Turtkebot3 robot. The experimental results show that our method outperforms other RVO extension methods on the three metrics.Originality/valueIn this paper, the authors propose navigation algorithms capable of adaptively selecting the optimal speed for a multi-robot system to avoid robot collisions during dynamic crowded interactions.

Detour-RS: Reroute Attack Vulnerability Assessment with Awareness of the Layout and Resource

Recent decades have witnessed a remarkable pace of innovation and performance improvements in integrated circuits (ICs), which have become indispensable in an array of critical applications ranging from military infrastructure to personal healthcare. Meanwhile, recent developments have brought physical security to the forefront of concern, particularly considering the valuable assets handled and stored within ICs. Among the various invasive attack vectors, micro-probing attacks have risen as a particularly menacing threat. These attacks leverage advanced focused ion beam (FIB) systems to enable post-silicon secret eavesdropping and circuit modifications with minimal traceability. As an evolved variant of micro-probing attacks, reroute attacks possess the ability to actively disable built-in shielding measures, granting access to the security-sensitive signals concealed beneath. To address and counter these emerging challenges, we introduce a layout-level framework known as Detour-RS. This framework is designed to automatically assess potential vulnerabilities, offering a systematic approach to identifying and mitigating exploitable weaknesses. Specifically, we employed a combination of linear and nonlinear programming-based approaches to identify the layout-aware attack costs in reroute attempts given specific target assets. The experimental results indicate that shielded designs outperform non-shielded structures against reroute attacks. Furthermore, among the two-layer shield configurations, the orthogonal layout exhibits better performance compared to the parallel arrangement. Furthermore, we explore both independent and dependent scenarios, where the latter accounts for potential interference among circuit edit locations. Notably, our results demonstrate a substantial near 50% increase in attack cost when employing the more realistic dependent estimation approach. In addition, we also propose time and gas consumption metrics to evaluate the resource consumption of the attackers, which provides a perspective for evaluating reroute attack efforts. We have collected the results for different categories of target assets and also the average resource consumption for each via, required during FIB reroute attack.

Gaze and behavioural metrics in the refractive correction of presbyopia.

To investigate gaze and behavioural metrics at different viewing distances with multifocal contact lenses (MFCLs), single vision contact lenses (SVCLs) and progressive addition lenses (PALs). Fifteen presbyopic contact lens wearers participated over five separate study visits. At each visit, participants were randomly assigned to wear one of five refractive corrections: habitual PAL spectacles, delefilcon A (Alcon Inc.) MFCLs and three separate pairs of delefilcon A single vision lenses worn as distance, intermediate and near corrections. Participants wore a Pupil Core headset to record eye and head movements while performing three visual tasks: reading, visual search and scene observation. Data were investigated using linear regression and post-hoc testing. Parameters of interest included gaze (fixation duration, head movement) and behavioural (reading speed, reading accuracy, visual search time) metrics. Reading speed in SVCLs was significantly faster than in MFCLs and PAL spectacles (F = 16.3, p < 0.0001). Refractive correction worn did not influence visual search times (F = 0.16, p = 0.85). Fixation duration was significantly affected by the type of visual task (F = 60.2, p < 0.001), and an interaction effect was observed between viewing distance and refractive correction (F = 4.3, p = 0.002). There was significantly more horizontal and vertical head movement (F = 3.2, p = 0.01 and F = 3.3, p = 0.01, respectively) during visual search tasks when wearing PAL spectacles compared to SVCLsor MFCLs. This work showed that the type of refractive correction affects behavioural metrics such as reading speed and gaze behaviour by affecting horizontal and vertical head movements. The findings of this study suggest that under certain conditions, wearers of MFCLs make fewer head movements compared to PAL spectacles. Gaze behaviour metrics offer a new approach to compare and understand contact lens and spectacle performance, with potential applications including peripheral optical designs for myopia management.

Limited English Proficiency and Graves’ Disease at an Urban Safety Net Hospital

IntroductionDecision-making regarding definitive therapy for Graves’ disease requires effective patient-provider communication. We investigated whether patients with limited English proficiency have differences in thyroidectomy outcomes or perioperative management when compared to English proficient (EP) patients at a safety net hospital with high-volume endocrine surgery practice. MethodsRetrospective study of patients who underwent thyroidectomy (2012-2021) for Graves’ disease within a tertiary referral system. Demographics, preoperative factors, and postoperative outcomes were abstracted via chart review and compared between EP and limited English proficient (LEP) patients in univariate analyses. Odds of postoperative complications were assessed via multivariable logistic regression. Time metrics such as time from endocrinology consultation to surgery were compared via Kaplan–Meier analysis and adjusted Cox proportional regression models. ResultsOf 236 patients, 85 (36%) had LEP. Low and equivalent complication rates occurred across language groups (<1% permanent). LEP patients had similar odds of thyroidectomy-specific complications (odds ratio = 1.2; 95% confidence interval 0.6-2.4). Adjusted Cox proportional hazards ratios showed that LEP patients experienced significantly shorter time from endocrinology consultation to surgery compared to EP patients [hazard ratio = 0.7; 95% confidence interval 0.5-0.9]. ConclusionsThyroidectomy-specific complication rate for patients with Graves’ disease was low, and we detected no independent association between complications and English language proficiency. Non-English primary language was independently associated with reduced time from endocrinology consultation to surgery. This finding must be interpreted with nuance and is likely multifactorial. It may reflect a well-organized, efficient system for under-resourced patients, or it may derive from communication barriers that limit robust shared decision-making, thus accelerating time to surgery.

210 Defining the Learning Curve for Cerebral Angiography Using a Novel Web-Based Feedback Application and Head-Mounted Video Review

INTRODUCTION: The milestones that a resident or fellow should reach to achieve competence in cerebral angiography have yet to be defined. METHODS: Two neurosurgical residents were given a written knowledge exam before and after rotating on the endovascular service. Cerebral angiography diagnostic and treatment cases were prospectively collected in a novel web-based, multi-institutional feedback application. These residents performed cerebral angiography cases under graduated supervision while wearing head-mounted cameras for subsequent review with direct feedback. Residents and reviewers independently utilized CaseCurve, a novel neurosurgical resident performance tracking application, as a platform for performance rating. Numerical scores were assigned ranging from 1 (novice) to 5 (masterful) in the following categories: access, catheter navigation, table operation, closure, efficiency, finesse, angiographic interpretation, and catheter knowledge. The number of times the supervising surgeon had to take over the case was recorded. Arch anatomy and vessel tortuosity were graded. Operative videos were analyzed for operator inefficiencies and time metrics for sheath insertion and target vessel selection. Angiographic efficiency was assessed with fluoroscopic doses/times and procedural durations. RESULTS: Both residents demonstrated improvement in cognitive performance as measured by written assessment scores. Significant improvements were observed in technical performance as measured by numerical scores assigned by self-evaluation, faculty review, objective measures of angiographic efficiency, and the number of times the supervising surgeon had to take over the procedure. Concordance between faculty and resident self-evaluations was 74.2%, with residents underestimating their performance in 21% and overestimating in 5%. CONCLUSIONS: There is significant educational value for the trainer and trainee offered by our novel web-based feedback application and head-mounted video review. This data allows for accurate assessments of skill acquisition and delineation of appropriate milestones/learning categories for trainees in endovascular neurosurgery.

数据论文引用计量规律初探——基于中国数据期刊的案例研究

Data publishing provides an innovative model for data sharing. To gain better insights into data publishing, this paper uses a literature review and case studies to build up a citation metrics framework and conducts case studies on two Chinese data journals, Chinese Science Data and the Journal of Global Change Data and Discovery. This paper reveals the status of data publishing and reusability of the linked datasets in the two journals. And over thirty indicators have been analyzed, covering paper indicators (e.g. subjects, team size, and funding sources), data indicators (e.g. data sources, data scale, types, visits, and downloads), citation indicators (e.g. cited, citing, and time metrics), etc. The results unveil that, at the macro level, data publishing has become an important way for data sharing. It effectively promotes open cooperation, together with funding support, high data quality control, and facilitates the sharing of special collections of datasets across domains. Besides, national data centers play a vital role in supporting data publishing. At the micro level, datasets typically have strong associations with subjects but may still share commonalities across domains. Moreover, several indicators may contribute to decoding data reusability, such as combining data visits and downloads, and further links to citations. In addition, independent data publishing is on the rise in China, and we suggest nourishing data culture, diversifying business models, reinforcing data governance, developing robust platforms and technologies, and better incentives and metrics for data sharing in the long run.​

Service delivery in acute ischemic stroke patients: Does sex matter?

Women with acute ischemic stroke (AIS) are older and have greater preexisting handicap than men. Given that these factors do not fully explain their poorer long-term outcomes, we sought to investigate potential sex differences in the delivery of acute stroke care in a large cohort of consecutive AIS patients. We analyzed all patients from ASTRAL (Acute Stroke Registry and Analysis of Lausanne) from March 2003 to December 2019. Multivariable analyses were performed on acute time metrics, revascularization therapies, ancillary examinations for stroke workup, subacute symptomatic carotid artery revascularization, frequency of change in goals of care (palliative care), and length of hospital stay. Of the 5347 analyzed patients, 45% were biologically female and the median age was 74.6 years. After multiple adjustments, female sex was significantly associated with higher onset-to-door (adjusted hazard ratio [aHR] = 1.09, 95% confidence interval [CI] = 1.04-1.14) and door-to-endovascular-puncture intervals (aHR = 1.15, 95% CI = 1.05-1.25). Women underwent numerically fewer diagnostic examinations (adjusted odds ratio [aOR] = 0.94, 95% CI = 0.85-1.04) and fewer subacute carotid revascularizations (aOR = 0.69, 95% CI = 0.33-1.18), and had longer hospital stays (aHR = 1.03, 95% CI = 0.99-1.07), but these differences were not statistically significant. We found no differences in the rates of acute revascularization treatments, or in the frequency of change of goals of treatments. This retrospective analysis of a large, consecutive AIS cohort suggests that female sex is associated with unfavorable pre- and in-hospital time metrics, such as a longer onset-to-door and door-to-endovascular-puncture intervals. Such indicators of less effective stroke care delivery may contribute to the poorer long-term functional outcomes in female patients and require further attention.

PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling

Pre-routing timing prediction has been recently studied for evaluating the quality of a candidate cell placement in chip design. It involves directly estimating the timing metrics for both pin-level (slack, slew) and edge-level (net delay, cell delay), without time-consuming routing. However, it often suffers from signal decay and error accumulation due to the long timing paths in large-scale industrial circuits. To address these challenges, we propose a two-stage approach. First, we propose global circuit training to pre-train a graph auto-encoder that learns the global graph embedding from circuit netlist. Second, we use a novel node updating scheme for message passing on GCN, following the topological sorting sequence of the learned graph embedding and circuit graph. This scheme residually models the local time delay between two adjacent pins in the updating sequence, and extracts the lookup table information inside each cell via a new attention mechanism. To handle large-scale circuits efficiently, we introduce an order preserving partition scheme that reduces memory consumption while maintaining the topological dependencies. Experiments on 21 real world circuits achieve a new SOTA R2 of 0.93 for slack prediction, which is significantly surpasses 0.59 by previous SOTA method. Code will be available at: https://github.com/Thinklab-SJTU/EDA-AI.

Unveiling the enigma: Exploring organizational dysfunctional learning modes and institutional amnesia in the Indian Universal Service Obligation Fund

AbstractDespite various government initiatives and substantial unutilized funds in the Indian Universal Service Obligation Fund (USF), a significant population in rural and remote areas still lack broadband services due to inadequate consideration for demographic and geographical challenges in planning and implementation strategies, mainly stemming from focus on quick electoral benefits, favoritism, organizational politics and a sense of indifference. This study employs a multiple‐case approach to analyze the USF institution using the proposed integrated organizational learning framework, offering a novel perspective. The proposed framework identifies factors influencing effective outcomes and provides a comprehensive assessment of cross‐project learning culture within the organization. Additionally, by unveiling systemic deception and hidden intentions, the application of this framework may act as a deterrent to improper practices, contributing to an improved outlook for future projects. Our analysis reveals that despite encountering distinct experiences and outcomes in different initiatives, the USF has missed opportunities to fully leverage lessons learned and avoid repeating past mistakes. To bolster the efficacy of future projects, we propose that the USF adopt a proactive organizational learning strategy involving thorough evaluations and analyses of past projects by experts. This process should aim to extract key lessons, challenges, and best practices, culminating in establishing a repository of learnings. Regularly updating and utilizing this repository can foster a culture of continuous improvement and informed decision‐making. Such an approach can improve project delivery across traditional metrics of time, cost, and quality and maximize benefits delivered to beneficiaries.

On Phase Transitions during Collisions near the Horizon of Black Holes

During particle collisions in the vicinity of the horizon of black holes, it is possible to achieve energies and temperatures corresponding to phase transitions in particle physics. It is shown that the sizes of the regions of the new phase are of the order of the Compton length for the corresponding mass scale. The lifetime is also on the order of the Compton time. It is shown that the inverse influence of the energy density in the electro-weak phase transition in collisions on the space–time metric can be neglected.

Hybrid Elastic Scaling Strategy for Container Cloud based on Load Prediction and Reinforcement Learning

To harness the advantages of both proactive and responsive scaling, adapting to various workload scenarios, this paper introduces a container hybrid scaling strategy called HyPredRL, rooted in load prediction and reinforcement learning. Within the proactive scaling module RL-PM, a load prediction model, MSC-LSTM, predict workloads and, in conjunction with current workload states, leverages reinforcement learning agents for intelligent scaling decisions. The responsive scaling strategy, SLA-HPA, enhances Kubernetes’ native scaling strategy, which primarily considers resource utilization, by incorporating response time metrics. Ultimately, a hybrid scaling controller is designed, applying the principles of “rapid scaling out” and “balanced conflicts” to coordinate proactive and responsive scaling. Experimental results demonstrate that HyPredRL outperforms existing methods in SLA violation rate, resource utilization, and request response time, effectively improving application performance and scalability.

Clinical and imaging markers for the prognosis of acute ischemic stroke.

Significant differences in the outcomes observed in patients with acute ischemic stroke (AIS) have led to research investigations for identifying the predictors. In this retrospective study, we aimed to investigate the relationship of different clinical and imaging factors with the prognosis of AIS. All clinical and imaging metrics were compared between the good and poor prognosis groups according to the modified Rankin Scale (mRS) score at 90 days after discharge. Clinical factors included gender, age, NIHSS scores at admission, and other medical history risk factors. Imaging markers included the lesion's size and location, diffusion, and perfusion metrics of infarction core and peripheral regions, and the state of collateral circulation. Spearman's correlations were analyzed for age and imaging markers between the different groups. The Chi-square test and Cramer's V coefficient analysis were performed for gender, collateral circulation status, NIHSS score, and other stroke risk factors. A total of 89 patients with AIS were divided into the good (mRS score ≤ 2) and poor prognosis groups (mRS score ≥ 3). There were differences in NIHSS score at the admission; relative MK (rMK), relative MD (rMD), relative CBF (rCBF) of the infarction core; relative mean transit time (rMTT), relative time to peak (rTTP), and relative CBF (rCBF) of peripheral regions; and collateral circulation status between the two groups (p < 0.05). Among them, the rMK of infarction lesions had the strongest correlation with the mRS score at 90 days after discharge (r = 0.545, p < 0.001). Perfusion and diffusion metrics could reflect the microstructure and blood flow characteristics of the lesion, which were the key factors for the salvage ability and prognosis of the infarction tissue. The characteristics of the infarction core and peripheral regions have different effects on the outcomes. Diffusion of infarction core has strong relations with the prognosis, whereas the time metrics (MTT, TTP) were more important for peripheral regions. MK had a more significant association with prognosis than MD. These factors were the primary markers influencing the prognosis of cerebral infarction patients.

Progression analysis versus traditional methods to quantify slowing of disease progression in Alzheimer’s disease

BackgroundThe clinical meaningfulness of the effects of recently approved disease-modifying treatments (DMT) in Alzheimer’s disease is under debate. Available evidence is limited to short-term effects on clinical rating scales which may be difficult to interpret and have limited intrinsic meaning to patients. The main value of DMTs accrues over the long term as they are expected to cause a delay or slowing of disease progression. While awaiting such evidence, the translation of short-term effects to time delays or slowing of progression could offer a powerful and readily interpretable representation of clinical outcomes.MethodsWe simulated disease progression trajectories representing two arms, active and placebo, of a hypothetical clinical trial of a DMT. The placebo arm was simulated based on estimated mean trajectories of clinical dementia rating scale–sum of boxes (CDR-SB) recordings from amyloid-positive subjects with mild cognitive impairment (MCI) from Alzheimer’s Disease Neuroimaging Initiative (ADNI). The active arm was simulated to show an average slowing of disease progression versus placebo of 20% at each visit. The treatment effects in the simulated trials were estimated with a progression model for repeated measures (PMRM) and a mixed model for repeated measures (MMRM) for comparison. For PMRM, the treatment effect is expressed in units of time (e.g., days) and for MMRM in units of the outcome (e.g., CDR-SB points). PMRM results were implemented in a health economics Markov model extrapolating disease progression and death over 15 years.ResultsThe PMRM model estimated a 19% delay in disease progression at 18 months and 20% (~ 7 months delay) at 36 months, while the MMRM model estimated a 25% reduction in CDR-SB (~ 0.5 points) at 36 months. The PMRM model had slightly greater power compared to MMRM. The health economic model based on the estimated time delay suggested an increase in life expectancy (10 months) without extending time in severe stages of disease.ConclusionPMRM methods can be used to estimate treatment effects in terms of slowing of progression which translates to time metrics that can be readily interpreted and appreciated as meaningful outcomes for patients, care partners, and health care practitioners.

Enhancing blood pressure management protocol implementation in patients with acute intracerebral haemorrhage through a nursing-led approach: A retrospective cohort study.

To evaluate the impact of nurse care changes in implementing a blood pressure management protocol on achieving rapid, intensive and sustained blood pressure reduction in acute intracerebral haemorrhage patients. Retrospective cohort study of prospectively collected data over 6 years. Intracerebral haemorrhage patients within 6 h and systolic blood pressure ≥ 150 mmHg followed a rapid (starting treatment at computed tomography suite with a target achievement goal of ≤60 min), intensive (target systolic blood pressure < 140 mmHg) and sustained (maintaining target stability for 24 h) blood pressure management plan. We differentiated six periods: P1, stroke nurse at computed tomography suite (baseline period); P2, antihypertensive titration by stroke nurse; P3, retraining by neurologists; P4, integration of a stroke advanced practice nurse; P5, after COVID-19 impact; and P6, retraining by stroke advanced practice nurse. Outcomes included first-hour target achievement (primary outcome), tomography-to-treatment and treatment-to-target times, first-hour maximum dose of antihypertensive treatment and 6-h and 24-h systolic blood pressure variability. Compared to P1, antihypertensive titration by stroke nurses (P2) reduced treatment-to-target time and increased the rate of first-hour target achievement, retraining of stroke nurses by neurologists (P3) maintained a higher rate of first-hour target achievement and the integration of a stroke advanced practice nurse (P4) reduced both 6-h and 24-h systolic blood pressure variability. However, 6-h systolic blood pressure variability increased from P4 to P5 following the impact of the COVID-19 pandemic. Finally, compared to P1, retraining of stroke nurses by stroke advanced practice nurse (P6) reduced tomography-to-treatment time and increased the first-hour maximum dose of antihypertensive treatment. Changes in nursing care and continuous education can significantly enhance the time metrics and blood pressure outcomes in acute intracerebral haemorrhage patients. STROBE guidelines. No Patient or Public Contribution.

Polling Sanitization to Balance I/O Latency and Data Security of High-density SSDs

Sanitization is an effective approach for ensuring data security through scrubbing invalid but sensitive data pages, with the cost of impacts on storage performance due to moving out valid pages from the sanitization-required wordline, which is a logical read/write unit and consists of multiple pages in high-density SSDs. To minimize the impacts on I/O latency and data security, this article proposes a polling-based scheduling approach for data sanitization in high-density SSDs. Our method polls a specific SSD channel for completing data sanitization at the block granularity, meanwhile other channels can still service I/O requests. Furthermore, our method assigns a low priority to the blocks that are more likely to have future adjacent page invalidations inside sanitization-required wordlines, while selecting the sanitization block, to minimize the negative impacts of moving valid pages. Through a series of emulation experiments on several disk traces of real-world applications, we show that our proposal can decrease the negative effects of data sanitization in terms of the risk-performance index, which is a united time metric of I/O responsiveness and the unsafe time interval, by 16.34% , on average, compared to related sanitization methods.

Cost optimization and machine learning-based prediction of waiting time for fault-tolerance machining system with general vacation and F-policy

This study focuses on the queueing management, performance analysis, and cost optimization of a fault tolerance machining system (FTMS) that incorporates the multiple vacations of repairman and admission control F-policy (ACFP). The ACFP is an effective tool for managing the congestion problem that arises due to the arrival of failed machines for repair jobs in a system. This effectively controls the influx of failed machines and enhances the efficiency of the repair process. Furthermore, when there are no failed machines available in the system (i.e., the system is empty), the repairman may choose to take a vacation. After returning from vacation, if there are no failed machines available for repair, the repairman can take another vacation. For the mathematical analysis of the proposed model, the Chapman–Kolmogorov (C-K) steady-state equations are constructed and solved. This is achieved by introducing supplementary variables corresponding to vacation times, followed by using the Laplace-Stieltjes transform (LST) and recursive method to establish probability distributions. Furthermore, using the established probability distributions, various performance measures are developed to gain insights into the system’s behavior. These performance measures include the expected number of failed machines in the system, waiting time, machine availability, and other relevant metrics. Numerical simulation is carried out to assess the influence of input parameters on performance measures. Moreover, a machine learning approach employing regression methods is employed to predict the waiting time for failed machines to initiate their repair within the system. This combination of numerical simulation and machine learning analysis provides a comprehensive understanding of the system’s performance and enables accurate predictions of waiting times. Cost optimization is also performed for the FTMS using trapezoidal fuzzy numbers as cost elements by employing meta-heuristic techniques based on particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms.

Enhanced methods for the weight constrained shortest path problem

AbstractThe classic problem of constrained pathfinding is a well‐studied, yet challenging, network optimization problem with a broad range of applications in various areas such as communication and transportation. The weight constrained shortest path problem (WCSPP), the base form of constrained pathfinding with only one side constraint, aims to plan a cost‐optimum path with limited weight/resource usage. Given the bi‐criteria nature of the problem (i.e., dealing with the cost and weight of paths), methods addressing the WCSPP have some common properties with bi‐objective search. This article leverages the recent state‐of‐the‐art techniques in both constrained pathfinding and bi‐objective search and presents two new solution approaches to the WCSPP on the basis of A* search, both capable of solving hard WCSPP instances on very large graphs. We empirically evaluate the performance of our algorithms on a set of large and realistic problem instances and show their advantages over the state‐of‐the‐art algorithms in both time and space metrics. This article also investigates the importance of priority queues in constrained search with A*. We show with extensive experiments on both realistic and randomized graphs how bucket‐based queues without tie‐breaking can effectively improve the algorithmic performance of exhaustive A*‐based bi‐criteria searches.

Optimizing Outpatient Radiation Oncology Consult Workflow by Using Time-Driven Activity-Based Costing: Efficiency and Financial Impacts.

Clinical efficiency is a key component of value-based health care. Our objective here was to identify workflow inefficiencies by using time-driven activity-based costing (TDABC) and evaluate the implementation of a new clinical workflow in high-volume outpatient radiation oncology clinics. Our quality improvement study was conducted with the Departments of GI, Genitourinary (GU), and Thoracic Radiation Oncology at a large academic cancer center and four community network sites. TDABC was used to create process maps and optimize workflow for outpatient consults. Patient encounter metrics were captured with a real-time status function in the electronic medical record. Time metrics were compared using Mann-Whitney U tests. Individual patient encounter data for 1,328 consults before the intervention and 1,234 afterward across all sections were included. The median overall cycle time was reduced by 21% in GI (19 minutes), 18% in GU (16 minutes), and 12% at the community sites (9 minutes). The median financial savings per consult were $52 in US dollars (USD) for the GI, $33 USD for GU, $30 USD for thoracic, and $42 USD for the community sites. Patient satisfaction surveys (from 127 of 228 patients) showed that 99% of patients reported that their providers spent adequate time with them and 91% reported being seen by a care provider in a timely manner. TDABC can effectively identify opportunities to improve clinical efficiency. Implementing workflow changes on the basis of our findings led to substantial reductions in overall encounter cycle times across several departments, as well as high patient satisfaction and significant financial savings.