Potential Delay Research Articles

Predicting Extension of Time and Increasing Contract Price in Road Infrastructure Projects Using a Sugeno Fuzzy Logic Model

Road infrastructure plays a crucial role in the development of countries, significantly influencing economic growth, social progress, and environmental sustainability. Major infrastructure projects are frequently challenged by substantial risks and uncertainties, leading to delays, budget overruns, and compromised quality. These issues can undermine the economic viability and efficiency of projects, making effective risk management essential for minimizing negative impacts and ensuring project success. For these reasons, a study was conducted using a Sugeno fuzzy logic system applied to completed projects. The resulting model is based on 10 project characteristics and provides highly accurate predictions for Extension of Time (EoT) and Increasing Contract Price (ICP). By utilizing this model, project management can be significantly improved through more accurate forecasting of potential delays and cost overruns. The high precision of the Sugeno fuzzy logic system enables better risk assessment and proactive decision-making, allowing project managers to implement targeted strategies to mitigate risks and optimize project outcomes.

The Impact of Smartpm's Ai-Driven Analytics on Predicting and Mitigating Schedule Delays in Complex Infrastructure Projects

This study explores the extent to which SmartPM's AI-driven analytics can predict and mitigate the potential impending schedule delays on complex infrastructure projects. From this study, the exhaustive research based on case study and associated data asserts that AI-driven predictive analytics can significantly leverage the outcomes of projects by bringing the identified potential delays to notice before such occurrences and provide actionable insight regarding the possible strategies to mitigate it.

Distributed control and passivity‐based stability analysis for time‐delayed DC microgrids

AbstractFor cooperation among distributed generations in a DC microgrid (MG), distributed control is widely applied. However, the delay in distributed communication will result in steady‐state bias and the risk of instability. This paper proposes a novel distributed control for time‐delayed DC MGs to achieve accurate current proportional sharing and weighted average voltage regulation. Firstly, by utilizing an advanced observer based on the PI consensus algorithm, the steady‐state bias problem is addressed. Then, using the passivity theory, stability analysis is conducted to reveal the principle of system instability caused by communication delay. On this basis, to offset the adverse effects of communication delay on the system stability, scattering transformation is introduced in the observer‐based distributed control. Moreover, considering the potential delay from the measurement stage in real‐life scenarios, the sufficient condition of the system stability is concluded by constructing the Lyapunov–Krasovskii functional. Finally, the performance of the proposed control and conclusions of stability analysis are verified by hardware‐in‐loop tests.

Delays Factors for Construction Projects in South of Iraq

This study seeks to identify and categorize the factors responsible for delays in construction projects in southern Iraq. Using a sequential research approach that includes a literature review, interviews, and surveys, data were collected from 115 industry professionals, including clients, consultants, project managers, contractors, and engineers. The analysis utilized both descriptive and conclusive statistical methods, with a specific emphasis on the Spearman rank correlation coefficient to verify the reliability of the delay factor rankings. The research identified 45 major delay factors, with the most critical being global and local economic crises, bureaucracy and corruption, public holidays, delays in securing municipal permits, and frequent change orders initiated by clients. These factors were further classified into eight categories: inaccuracies in the tendering process, technical performance management, government interference, rework in construction practices, among others. The results indicate strong consensus among the different respondent groups regarding the main causes of project delays, as demonstrated by high correlation coefficients and a Chi-squared test confirming the agreement's significance. The findings provide valuable insights for practical applications and academic research, supporting the selection of project leaders, anticipating potential delays, and improving project management practices in Iraq. The study suggests future research should focus on strategic economic planning, exploring alternative funding sources, combating corruption, and implementing regulatory reforms to address the identified delay factors.

Mass Balance in Pharmaceutical Stress Testing: A Review of Principles and Practical Applications.

Stress testing (also known as forced degradation) of pharmaceutical drug substances and products is a critical part of the drug development process, providing insight into the degradation pathways of drug substances and drug products. This information is used to support the development of stability-indicating methods (SIMs) capable of detecting pharmaceutically relevant degradation products that might potentially be observed during manufacturing, long-term storage, distribution, and use. Assessing mass balance of stressed samples is a key aspect of developing SIMs and is a regulatory expectation. However, the approaches to measure, calculate, and interpret mass balance can vary among different pharmaceutical companies. Such disparities also pose difficulties for health authorities when reviewing mass balance assessments, which may result in the potential delay of drug application approvals. The authors have gathered input from 10 pharma companies to map out apractical review of science-based approaches and technical details to assess and interpret mass balance results. Key concepts of mass balance are introduced, various mass balance calculations are demonstrated, and recommendations on how to investigate poor mass balance results are presented using real-world case studies. Herein we provide a single source reference on the topic of mass balance in pharmaceutical forced degradation for small molecule drug substances and drug products in support of regulatory submissions with the goal of facilitating a shared understanding among pharmaceutical scientists and health authorities.

Multi-modal transformer architecture for medical image analysis and automated report generation

Medical practitioners examine medical images, such as X-rays, write reports based on the findings, and provide conclusive statements. Manual interpretation of the results and report generation by examiners are time-consuming processes that lead to potential delays in diagnosis. We propose an automated report generation model for medical images leveraging an encoder–decoder architecture. Our model utilizes transformer architectures, including Vision Transformer (ViT) and its variants like Data Efficient Image Transformer (DEiT) and BERT pre-training image transformer (BEiT), as an encoder. These transformers are adapted for processing to extract and gain visual information from medical images. Reports are transformed into text embeddings, and the Generative Pre-trained Transformer (GPT2) model is used as a decoder to generate medical reports. Our model utilizes a cross-attention mechanism between the vision transformer and GPT2, which enables it to create detailed and coherent medical reports based on the visual information extracted by the encoder. In our model, we have extended the report generation with general knowledge, which is independent of the inputs and provides a comprehensive report in a broad sense. We conduct our experiments on the Indiana University X-ray dataset to demonstrate the effectiveness of our models. Generated medical reports from the model are evaluated using word overlap metrics such as Bleu scores, Rouge-L, retrieval augmentation answer correctness, and similarity metrics such as skip thought cs, greedy matching, vector extrema, and RAG answer similarity. Results show that our model is performing better than the recurrent models in terms of report generation, answer similarity, and word overlap metrics. By automating the report generation process and incorporating advanced transformer architectures and general knowledge, our approach has the potential to significantly improve the efficiency and accuracy of medical image analysis and report generation.

Multifaceted Strategy That Improves Students’ Achievement of Entrustable Professional Activities Across Advanced Pharmacy Practice Experiences

ObjectiveTo outline an approach to help students achieve Entrustable Professional Activities (EPAs) entrustment during a sequence of Advanced Pharmacy Practice Experiences (APPEs) by implementing longitudinal monitoring and individualized intervention and remediation strategies. MethodsUsing the recommended EPAs within the core APPEs (acute care, ambulatory care, community, institutional), students were expected to achieve entrustment on all EPAs by graduation. A longitudinal monitoring approach, using an “EPA report card,” was implemented to continuously identify students at risk of not meeting the EPA requirement of “Level 3” entrustment (perform with reactive supervision). Individualized interventions, including proactive outreach and in-sequence remediation, were incorporated into the APPE core and elective sequence to help ensure all students were entrusted by the end of APPEs without requiring further end-of-year remediation to graduate. ResultsFor the graduating classes of 2023 and 2024, 12% (8 of 69) and 16% (12 of 75) students, respectively, were identified as at risk of not meeting EPA entrustment. Proactive outreach, in-sequence remediation, or a combination of both strategies, were used to enhance learning and EPA performance. As a result, all students achieved “Level 3” entrustment on the deficient EPA(s) by the end of the APPE sequence. No student required further end-of-year remediation to graduate. ConclusionUtilizing a multifaceted strategy provided timely, real-world practice opportunities to improve the students’ achievement of EPAs across the APPE curriculum and decreased the need for end-of-year remediation and potential graduation delays.

ENHANCED BRAIN CANCER DETECTION IN MRI SCANS THROUGH TEMPLATE REGRESSION SIAMESE REGIONAL PROPOSED NETWORK FOR SEGMENTATION AND FUZZY LOGIC FUSION OF SEGMENTED REGIONS

Accurate detection and segmentation of brain cancer in MRI scans are critical for effective diagnosis and treatment planning. Traditional methods often struggle with the complexities of tumor morphology and variations in scan quality. Existing detection systems can be slow and may not effectively handle the variability in tumor appearances, leading to potential delays in diagnosis and treatment. To address these challenges, we propose an enhanced detection framework using a Siamese Regional Proposed Network (SRPN). The SRPN integrates template branch and bounding box regression to expedite detection processes. The system utilizes an extended Siamese network to learn the distance between tracklet pairs, capturing the local and global features of tumors. These features are transferred to bidirectional gated recurrent units (GRUs), which generate tracklets and segment them into shorter sub-tracklets based on local distances. The segmented sub- tracklets are then reconnected into longer trajectories using similarities derived from temporal pooling global features. Additionally, fuzzy logic fusion is employed to combine segmented regions for improved accuracy. The SRPN-based framework demonstrated a significant improvement in detection speed and accuracy. Experimental results show an accuracy increase of 12% over traditional methods, achieving 94% accuracy with a detection time reduction of 30%. The system also improved segmentation precision, with a mean Intersection over Union (IoU) score of 85%, compared to 75% in conventional approaches.

Tribalism in the workplace: Anesthesia providers and others differing perceptions on causes of scheduling delay

PurposeOperating an interventional radiology (IR) service takes the cooperation of a team of physicians, advanced practice providers, nurses, technologists, and anesthesia providers. Addressing delays in patient flow through IR takes the cooperation of all these groups. To measure the varying perceptions of the causes of these delays, IR team members were surveyed regarding the frequency and duration of potential sources of delay. MethodsA 13-question survey regarding perceived causes of potential delays was sent to all staff employed at a single academic center interventional radiology service. The survey responses were grouped by their role on the team: IR practitioners, technologists, nurses, and anesthesia providers. Intergroup rankings were compared using a Tau test. ResultsThere were 47 respondents out of a potential pool of 73 (64%). Significant differences were found between anesthesia providers and all three other groups regarding the most frequent and lengthy causes of pre-procedure and intra-procedure delays—the three other groups all perceived anesthesia delays as more common and severe (P values of 0.001, 0.001, and 0.009 for LIPs, nurses, and techs, respectively). Similarly, LIPs reported significantly different perceptions of the causes of most frequent delays against all 3 other groups (P values of 0.009, 0.009, and 0.001 for nurses, techs, and CRNAs respectively). Each group tended to rank their contributions to delays as less frequent and lengthy than all other groups, as evidenced by the fact that 8/12 pre-procedure rankings attributable to one group showed evidence of this pattern. ConclusionThis survey, at a single institution, revealed significant differences in opinion about the causes of procedure delays with individual groups tending to blame themselves much less for errors than others. Although not generalizable, these results suggest a source of inter-group stress that may be mitigated with improved communication, changes in education, and training in perspective-taking.

Optimal Profitability Analysis for Contractors Regarding Variations in Payment Systems and Scheduling

Construction projects often face higher risks than other types of projects, primarily due to potential delays from inaccurate scheduling and cash flow issues. Therefore, effective scheduling and payment systems are crucial for contractors to reduce risks and achieve optimal profitability. This study aims to identify optimal cash flow and profitability for contractors through the analysis of various payment systems and the utilization of float time. The methods used include cash flow analysis and feasibility analysis using NPV, BCR, and ROI methods. This research explores eight different payment and scheduling approaches to evaluate their impact on project profitability. The results show that cash flow across all alternatives remains positive without experiencing a shortage of funds and indicate that the contractor's optimal profitability is achieved with application of alternative 5, featuring 0% float time and no advance payments. This alternative results in the highest profit, with an NPV of Rp 15,338,960,581, a BCR of 1.059, and an ROI of 5.90%. From these results, it can be concluded that not utilizing float time leads to the most optimal profitability for the contractor. Additionally, it can be said that the scheduling plan implemented by the contractor for the Twin Tower project is well-structured. However, for the payment system, it is recommended to adopt a no-advance payment approach. It is hoped that this study will help contractors optimize their profits in the competitive construction industry.

The impact of COVID-19 on colorectal cancer in regional Northern Australia and changes to treatment related to distance to care: a retrospective cohort study.

The COVID-19 pandemic has created challenges in the diagnosis and management of colorectal cancer (CRC). It was proposed in regional Northern Australia that the distance to services could further impact cancer outcomes, leading to delayed diagnosis. The authors compared the outcomes of patients prior and during the pandemic; with a focus on whether patients were presenting in the emergency setting with more advanced disease. The distance to treatment was also analysed to see if there was any impact to the management of patients with colorectal cancer. A retrospective analysis of 444 patients who underwent treatment for colorectal cancer over two time periods was examined. Time period 1 (prior to COVID-19); March 2017-July 2019 and time period 2 (during COVID-19); March 2020-July 2022. Only patients with colorectal adenocarcinoma were included if they were primarily treated at a hospital in northern Australia; those with benign pathologies or recurrent disease were also excluded. Data was collected in terms of treatment and outcomes and compared between the two groups. A separate analysis of whether locality affected outcomes and referral times was also performed. In the time period prior to COVID-19, 174 patients' required invasive management, while in the second time period during COVID-19, there were 188 patients managed surgically or endoscopically. Of the patients managed prior to COVID-19 17/174 (9.8%) patients required emergency interventions, during COVID-19 this number increased to 37/188 (19.7%). This difference was deemed to be statistically significant (P =0.008). No substantial difference in cancer staging at presentation was found between the two groups. There was an increase in complication rate found during COVID-19 34.6 vs 25.5% prior to COVID-19 (P=0.046). During COVID-19, the median time between General Practitioner (primary care physician) referral and colonoscopy was actually lower than prior to covid 26.5 vs 36 days (P=0.047). When comparing local to distant patients, we found locally based patients had lower rates of neoadjuvant treatment 18.9 vs 30.4% (P=0.018) and higher rates of open surgery 39.1 vs 26.5%, P =0.012. An increase in time between colonoscopy and outpatient department review (OPD) was seen in patients not from the local area 13 vs 18 days (P =0.006). The authors found during the COVID-19 pandemic a greater proportion of patients were presenting with colorectal cancer that required emergency intervention. This may be due to decreased presentations to general practitioners due to lockdown causing potential delays in diagnosis. The authors did not see more advanced disease in these patients presenting emergently, between the two groups. Further assessment of local patients' vs patients from distant sites, showed difference in how patients were managed but similar outcomes. Our large catchment area with distance to treatment in Northern Australia may further impact the management of colorectal cancer in the future.

Circulating Liquid Biopsy Biomarkers in Glioblastoma: Advances and Challenges.

Gliomas, particularly glioblastoma (GBM), represent the most prevalent and aggressive tumors of the central nervous system (CNS). Despite recent treatment advancements, patient survival rates remain low. The diagnosis of GBM traditionally relies on neuroimaging methods such as magnetic resonance imaging (MRI) or computed tomography (CT) scans and postoperative confirmation via histopathological and molecular analysis. Imaging techniques struggle to differentiate between tumor progression and treatment-related changes, leading to potential misinterpretation and treatment delays. Similarly, tissue biopsies, while informative, are invasive and not suitable for monitoring ongoing treatments. These challenges have led to the emergence of liquid biopsy, particularly through blood samples, as a promising alternative for GBM diagnosis and monitoring. Presently, blood and cerebrospinal fluid (CSF) sampling offers a minimally invasive means of obtaining tumor-related information to guide therapy. The idea that blood or any biofluid tests can be used to screen many cancer types has huge potential. Tumors release various components into the bloodstream or other biofluids, including cell-free nucleic acids such as microRNAs (miRNAs), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), proteins, extracellular vesicles (EVs) or exosomes, metabolites, and other factors. These factors have been shown to cross the blood-brain barrier (BBB), presenting an opportunity for the minimally invasive monitoring of GBM as well as for the real-time assessment of distinct genetic, epigenetic, transcriptomic, proteomic, and metabolomic changes associated with brain tumors. Despite their potential, the clinical utility of liquid biopsy-based circulating biomarkers is somewhat constrained by limitations such as the absence of standardized methodologies for blood or CSF collection, analyte extraction, analysis methods, and small cohort sizes. Additionally, tissue biopsies offer more precise insights into tumor morphology and the microenvironment. Therefore, the objective of a liquid biopsy should be to complement and enhance the diagnostic accuracy and monitoring of GBM patients by providing additional information alongside traditional tissue biopsies. Moreover, utilizing a combination of diverse biomarker types may enhance clinical effectiveness compared to solely relying on one biomarker category, potentially improving diagnostic sensitivity and specificity and addressing some of the existing limitations associated with liquid biomarkers for GBM. This review presents an overview of the latest research on circulating biomarkers found in GBM blood or CSF samples, discusses their potential as diagnostic, predictive, and prognostic indicators, and discusses associated challenges and future perspectives.

Model adaptive phase space reconstruction.

Phase space reconstruction (PSR) methods allow for the analysis of low-dimensional data with methods from dynamical systems theory, but their application to prediction models, such as those from machine learning (ML), is limited. Therefore, we here present a model adaptive phase space reconstruction (MAPSR) method that unifies the process of PSR with the modeling of the dynamical system. MAPSR is a differentiable PSR based on time-delay embedding and enables ML methods for modeling. The quality of the reconstruction is evaluated by the prediction loss. The discrete-time signal is converted into a continuous-time signal to achieve a loss function, which is differentiable with respect to the embedding delays. The delay vector, which stores all potential embedding delays, is updated along with the trainable parameters of the model to minimize prediction loss. Thus, MAPSR does not rely on any threshold or statistical criterion for determining the dimension and the set of delay values for the embedding process. We apply the MAPSR method to uni- and multivariate time series stemming from chaotic dynamical systems and a turbulent combustor. We find that for the Lorenz system, the model trained with the MAPSR method is able to predict chaotic time series for nearly seven to eight Lyapunov time scales, which is found to be much better compared to other PSR methods [AMI-FNN (average mutual information-false nearest neighbor) and PECUZAL (Pecora-Uzal) methods]. For the univariate time series from the turbulent combustor, the long-term cumulative prediction error of the MAPSR method for the regime of chaos stays between other methods, and for the regime of intermittency, MAPSR outperforms other PSR methods.

Transit fares integrating alternative modes as a delay insurance

Public transport (PT) fare policy remains subject to innovations, not least evident in the Mobility as a Service discussion. Mode integration and related fare strategies can be used to increase the attractiveness of PT by compensating for potential delays. This study proposes “premium fares” as a novel pricing tool that can evaluate and improve the travel time reliability on a multimodal transportation network. The premium fare is higher than the standard fare but allows passengers to use an alternative service free of charge if waiting for the delayed public transport service is anticipated to be longer than a certain qualification threshold. Properties that guarantee monotonicity of the premium fare with respect to distance traveled are developed. The operator aims to find the premium fare price and qualification threshold that can maximize its profit, based on the probability distributions of delay and passengers’ value of time (VOT). We model this optimization problem for a railway line given a limited capacity of alternative mode services, e.g., the number of taxis. A two-stage approach using nonlinear and dynamic programming is developed to obtain the optimal decision variables and associated capacity allocation plan. Our results show that the introduction of the premium fare can benefit both operators and travelers with increased profits and improved travel time reliability. The interplay between fares and the qualification threshold is illustrated using various delay and VOT distributions. To attract enough customers the premium fare has to be set below a specific level dependent on the VOT distribution. Meanwhile, the operator adjusts the qualification threshold to control the cost paid to the alternative service provider.

Navigating The Cloud: Enhancing Human-Robot Collaboration In Industry 4.0 Through Digital Twins And Cloud-Based Systems

Industry 4.0 Is Geared Towards Establishing Sophisticated Industrial Ecosystems That Necessitate Secure And Efficient Collaboration Between Humans And Robots, Termed Human-Robot Collaboration (HRC). Within This Framework, HRC Leverages Cutting-Edge Collision Avoidance Technology Capable Of Detecting Objects And Foreseeing Possible Collisions, Thereby Calculating Alternative Pathways To Avert Any Direct Contact. Furthermore, The Concept Of Digital Twins Emerges As An Innovative Solution, Offering A Platform To Simultaneously Evaluate The Consequences Of Various Control Strategies Within A Digitally Simulated Environment. While Cloud Computing Infrastructure Can Furnish The Requisite Computational Prowess, It May Simultaneously Introduce Challenges Such As Increased Latency And Variability In Communication, Known As Jitter, Which Could Potentially Hinder System Performance. Cloud Technologies Are Renowned For Their Inventive Approaches To Ease The Workload For Software Developers And System Operators, Raising Questions About Their Integration With Digital Twin Methodologies And The Resilience Of Robotic Systems Against The Potential Delays Incurred From Cloud-Based Services. This Research Endeavors To Navigate These Complexities By Integrating A Blend Of Both Public And Private Cloud Services, Which Are Distinguished By Their Unique Parallel Processing Capabilities. This Study's Contributions Are Threefold. Firstly, It Introduces A Novel Method Designed To Gauge The Efficacy Of Diverse Strategies By Focusing On Their Associated Latency Impacts. Secondly, It Delineates A Tangible Application Of HRC, Illuminating Its Practical Benefits And Real-World Applicability. Lastly, It Defines A Crucial Performance Metric Intended To Assess The Efficiency Of These Systems Thoroughly. By Delving Into These Aspects, The Research Aims To Conduct A Comprehensive Analysis Of The Strengths And Weaknesses Inherent In Various Technological Methodologies And Their Consequential Effects On The Performance Of HRC Systems Within The Ambit Of Industry 4.0. Through This Detailed Examination, The Study Seeks To Provide Valuable Insights Into Optimizing Human-Robot Collaboration In Industrial Settings, Ensuring Both Security And Efficiency Are Upheld

Primary-Side Indirect Control of the Battery Charging Current in a Wireless Power Transfer Charger Using Adaptive Hill-Climbing Control Technique

This paper addresses the control task of a wireless power transfer (WPT) charger designed for electric vehicles (EVs). The challenge is to maintain a constant battery charging current when the WPT is controlled on the ground side. Indeed, the intermittent latency involved in the wireless data communication between the ground and vehicle sides leads to system instability. To overcome this issue, a new control approach has been proposed in this paper. The proposed technique ensures indirect control of the battery charging current through control of the current on the ground side. The control technique relies on an adaptive hill-climbing algorithm in conjunction with a PI-based controller. The adaptive parameter is adjusted online, during the operation of the charger, only when a new measure of the battery charging current is received on the primary side. This makes it possible to avoid the need for real-time wireless data communication. It should be noted that this aspect is crucial in ensuring the controller’s robustness and stability of the system regardless of potential delays in wireless communication and large misalignments between the coils. The validity of the proposed control technique has been confirmed through simulation. In addition, experimental validation, using a laboratory test bed, demonstrated satisfactory results.

Artificial Intelligence Modeling and Priapism.

This narrative review aims to outline the current available evidence, challenges, and future perspectives of Artificial Intelligence (AI) in the diagnosis and management of priapism, a condition marked by prolonged and often painful erections that presents unique diagnostic and therapeutic challenges. Recent advancements in AI offer promising solutions to face the challenges in diagnosing and treating priapism. AI models have demonstrated the potential to predict the need for surgical intervention and improve diagnostic accuracy. The integration of AI models into medical decision-making for priapism can also predict long-term consequences. AI is currently being implemented in urology to enhance diagnostics and treatment work-up for various conditions, including priapism. Traditional diagnostic approaches rely heavily on assessments based on history, leading to potential delays in treatment with possible long-term sequelae. To date, the role of AI in the management of priapism is understudied, yet to achieve dependable and effective models that can reliably assist physicians in making decisions regarding both diagnostic and treatment strategies.

Multifunctional Dopamine-Based Hydrogel Microneedle Electrode for Continuous Ketone Sensing.

Diabetic ketoacidosis (DKA), a severe complication of type 1 diabetes (T1D), is triggered by production of large quantities of ketone bodies, requiring patients with T1D to constantly monitor their ketone levels. Here, a skin-compatible hydrogel microneedle (HMN)-continuous ketone monitoring (HMN-CKM) device is reported. The sensing mechanism relies on the catechol-quinone chemistry inherent tothe dopamine (DA) molecules that are covalently linked to the polymer structure of the HMN patch. The DA serves the dual-purpose of acting as a redox mediator for measuring the byproduct of oxidation of 3-beta-hydroxybutyrate (β-HB), the primary ketone bodies; while, also facilitating the formation of a crosslinked HMN patch. A universal approach involving pre-oxidation and detection of the generated catechol compounds is introduced to correlate the sensor response to the β-HB concentrations. It is further shown that real-time tracking of a decrease in ketone levels of T1D rat model is possible using the HMN-CKM device, in conjunction with a data-driven machine learning model that considers potential time delays.

Formulation of train routing selection problem for different real-time traffic management objectives

The train routing selection problem (TRSP) addresses the optimized selection of alternative routes as a preliminary step for real-time railway traffic management problem (rtRTMP). In the TRSP, route selection relies on estimating potential delays resulting from scheduling decisions. The selected routes are then exclusively applied in the rtRTMP. While prior research established the mathematical model and solution algorithms for the TRSP, its practical application in real-time rail traffic management remains limited. The existing TRSP model focuses on a single objective function for the rtRTMP. However, in practice, various stakeholders may prioritize different objectives, leading to diverse objective functions employed in the rtRTMP. This paper extends the TRSP model by considering a range of suitable objectives for the rtRTMP. We formulate the TRSP for each objective function and enhance the cost estimation model to evaluate the correspondence between the TRSP and rtRTMP objective functions. We then assess the overall effectiveness of the TRSP for the rtRTMP through an evaluation that takes into account several configurations of the model and the rtRTMP solution approach used. Our purpose is to enlarge the applicability of the TRSP and enhance the efficiency of the rtRTMP for real-world systems. The paper includes an in-depth computational analysis of two French case studies to investigate the performance of the TRSP across different rtRTMP configurations.

Safety and Efficacy of Conscious Sedation Versus General Anesthesia for Distal Vessel Thrombectomy

BACKGROUND AND OBJECTIVES: Anesthesia modality for endovascular thrombectomy (EVT) for distal and medium vessel occlusions remains an open question. General anesthesia (GA) may offer advantages over conscious sedation (CS) because of reduced patient movement facilitating catheter navigation, but concerns persist about potential delays and hypotension affecting collateral circulation. METHODS: In our prospectively maintained stroke registry from December 2014 to July 2023, we identified patients with distal and medium vessel occlusions defined as M2, M3, or M4 occlusion; A1 or A2 occlusion; and P1 or P2 occlusion, who underwent EVT for acute ischemic stroke. We compared patients who received CS with those who received GA. Primary outcomes were early neurological improvement (ENI), successful reperfusion, first-pass effect, and good outcome at 90 days. Secondary outcomes included intracerebral hemorrhage, subarachnoid hemorrhage, and 90-day mortality. RESULTS: Of 279 patients, 69 (24.7%) received GA, whereas 193 (69.2%) received CS. CS was associated with higher odds of ENI compared with GA (odds ratio [OR] 2.59, 95% CI [1.04-6.98], P < .05). CS was also associated with higher rates of successful reperfusion (OR 2.33, 95% CI [1.11-4.93], P < .05). CS nonsignificantly trended toward lower rates of mortality (OR 0.51, 95% CI [0.2-1.3], P = .16). No differences in good outcome at 90 days, intracerebral hemorrhage, subarachnoid hemorrhage, or first-pass effect were seen. CONCLUSION: The use of CS during EVT seems to be safe and feasible with regard to successful recanalization, hemorrhagic complications, clinical outcome, and mortality. In addition, it may be associated with a higher rate of ENI. Further randomized studies in this specific EVT subpopulation are warranted.