Delay Analysis Research Articles

Delay analysis of pulmonary tuberculosis in the eastern coastal county of China from 2010 to 2021: evidence from two surveillance systems.

Tuberculosis (TB) remains a major public health challenge. However, indicators of delays in assessing effective TB prevention and control and its influencing factors have not been investigated in the eastern coastal county of China. All notified pulmonary tuberculosis (PTB) cases in the Fenghua District, China were collected between 2010 and 2021 from the available TB information management system. Comparison of delays involving patient, health system, and total delays among local and migrant cases. Additionally, in correlation with available Basic Public Health Service Project system, we performed univariate and multivariate logistic regression analyses identified the influencing factors associated with patient and total delays in patients aged >60 years. In total, 3,442 PTB cases were notified, including 1,725 local and 1,717 migrant patients, with a male-to-female ratio of 2.13:1. Median patient and total delays of local TB patients were longer than those for migrant patients; the median health system delay did not show any significant difference. For patient delay among the older adult, female (cOR: 1.93, 95% CI: 1.07-3.48), educational level of elementary school and middle school (cOR: 0.23, 95% CI: 0.06-0.84) had a statistical difference from univariable analysis; however, patients without diabetes showed a higher delay for multiple-factor analysis (aOR: 2.12, 95% CI: 1.02-4.41). Furthermore, only the education level of elementary school and middle school presented a low total delay for both univariate (cOR: 0.22, 95% CI: 0.06-0.82) and multivariate analysis (aOR: 0.21, 95% CI: 0.05-0.83) in the older patients. The delay of TB cases among migrants was lower than the local population in the Fenghua District, which may be related to the "healthy migrant effect". It highlights that women, illiterate people, and people without diabetes are key groups for reducing delays among older adults. Health awareness should focus on these target populations, providing accessible health services, and reducing the time from symptom onset to diagnosis.

A data analytics pipeline to optimize polymer dose strategy in a semi-continuous multi-feed dewatering system

Automated application of scientific data analytics is a critical element to enhance industrial productivity and minimize human errors. In this study a dynamic polymer dosing strategy is developed for an industrial centrifuge system receiving highly variable rates and quality of feed solids from a wastewater treatment plant. A four-section methodology is developed and tested at full-scale containing (i) data extraction for data collection and aggregation; (ii) Data wrangling including delay analysis and batch analysis; (iii) model development with predictive ability; (iv) model analysis for model evaluation and interpretation. A partial least squares and a random forest model were validated and used to predict polymer dosages. In contrast to PLS, the RF model was capable of learning structural information and describe which products related to increases or decreases in polymer dosage. An additional analysis investigating the impact of different product codes on the polymer dosage is presented, revealing that certain products generally lead to consistent changes in the polymer dosage. The proposed approach could potentially save operators 3–6 h a day in terms of time spent on manually adjusting polymer dosages. The presented methodology for data pipelining and analysis has a generic nature and easily exportable to other case studies.

RULE-BASED DELAY ANALYSIS SELECTION SYSTEM

Since the selection of a delay analysis method depends on the delay analyst's subjective judgment, there is a risk of making problematic decisions due to the delay analyst's ignorance, lack of experience, carelessness, or intention. Each one of these methods may yield different analysis results. Therefore, it is necessary to develop an objective decision-making system based solely on the project environment. The existing delay analysis methods are classified into four types, according to the basic principles used in the analysis. Seven conditions that affect the selection of a delay analysis method (i.e., type of schedule, type of information, timing of analysis, expectations from the delay analysis, acceptance by courts of law, capabilities of delay analysis team, and budget constraint) were identified in a literature review. A delay analysis selection system (DASS) was developed using if-then rules constructed by considering which conditions should exist in a given project environment for successfully performing a specific type of delay analysis and getting realistic results. This system was computerized and validated by implementing it in several but different scenarios considered in this study. The contributions of this study include (1) an objective selection decision can be made based on only the project environment, without relying on the analyst's subjective preference, and (2) unnecessary and expensive legal disputes caused by delay-related issues between construction owners and construction companies can be minimized.

System Dynamics as an Assistive Tool to Delay Analysis in Identifying Productivity Losses

It is well established in the construction literature that change orders have negative impacts on project productivity. Such impacts have been measured through statistical models and data gathered from a large number of projects, which enables drawing conclusions on a broad level. However, there is a gap when it comes to modeling the relationships between change orders and construction productivity on the project level. This paper proposes a novel framework using system dynamics (SD) modeling to complement current delay analysis techniques in quantifying the impacts of owners’ change orders on project productivity. SD was used as the modeling tool for its ability in capturing rippled impacts and model complex systems. The research methodology (1) identifies the exogenous and endogenous parameters, (2) develops a dynamic hypothesis that guides model formulation, (3) develops the SD model along with its mathematical formulation, (4) runs verification tests, and (5) calibrates the model and tests it on a real case study. The developed model captures the typical construction cycle and can quantify the impact of each change order separately. In addition, after calibrating the model to any project, multiple what-if scenarios are conducted to provide further insights on allocating delay responsibilities. This model can identify the delays caused due to change orders that can be helpful for both contractors and owners to resolve the corresponding claims prior to escalation to disputes.

A joint cluster-based RRM and Low-latency framework using the full-duplex mechanism for NR-V2X networks

The radio resource management (RRM) problem in new radio vehicle-to-everything (NR-V2X) communication systems is a combinatorial optimization problem, and it is hard to achieve an optimum result in polynomial time. To reduce the complexity, linear algorithms or meta-heuristics can solve the problem. In this study, we proposed a joint cluster-based resource management and low-latency framework using a full-duplex mechanism (JCRRM-FD) for NR-V2X networks, which is used for vehicle-to-vehicle (V2V)/ device-to-device (D2D) and vehicle-to-infrastructure (V2I) transmission in NR-V2X networks. The delay, resource management, and system throughput analysis of the full-duplex mechanism in this framework was performed to deal with resource utilization and latency constraints. Moreover, joint cluster-based radio resource management and ant colony optimization (ACO) algorithms were proposed to manage and optimize resources efficiently to achieve user separation. The swarm intelligence algorithm is a standard meta-heuristic algorithm employed to deal with the optimization concern by exploiting the V2X communication network’s unlimited speed (maximizing the data transfer capacity and overall network performance) while considering the quality-of-service (QoS) requirements. A comprehensive experiment analysis was enacted to evaluate the efficiency of the developed JCRRM-FD framework with baseline approaches. Based on the simulation results, the proposed JCRRM-FD framework enhances the fairness index, average delay, packet drop rate, best cost value, CDF, and throughput compared to the benchmark approaches.

Delay Analysis in Closed-Loop EEG Phase-Triggered Transcranial Magnetic Stimulation.

The recent development of closed-loop EEG phase-triggered transcranial magnetic stimulation (TMS) has advanced potential applications of adaptive neuromodulation based on the current brain state. Closed-loop TMS involves instantaneous acquisition of the EEG rhythm, timing prediction of the target phase, and triggering of TMS. However, the accuracy of EEG phase prediction algorithms is largely influenced by the system's transport delay, and their relationship is rarely considered in related work. This paper proposes a delay analysis that considers the delay of the closed-loop EEG phase-triggered TMS system as a primary factor in the validation of phase prediction algorithms. An in-silico validation using real EEG data was performed to compare the performance of commonly used algorithms. The experimental results indicate a significant influence of the total delay on the algorithm performance, and the performance ranking among algorithms varies at different levels of delay. We conclude that the delay analysis framework should be widely adopted in the design and validation of phase prediction algorithms for closed-loop EEG phase-triggered TMS systems.

Pattern formation and bifurcation analysis of delay induced fractional-order epidemic spreading on networks

The spontaneous emergence of ordered structures, known as Turing patterns, in complex networks is a phenomenon that holds potential applications across diverse scientific fields, including biology, chemistry, and physics. Here, we present a novel delayed fractional-order susceptible–infected–recovered–susceptible (SIRS) reaction–diffusion model functioning on a network, which is typically used to simulate disease transmission but can also model rumor propagation in social contexts. Our theoretical analysis establishes the Turing instability resulting from delay, and we support our conclusions through numerical experiments. We identify the unique impacts of delay, average network degree, and diffusion rate on pattern formation. The primary outcomes of our study are: (i) Delays cause system instability, mainly evidenced by periodic temporal fluctuations; (ii) The average network degree produces periodic oscillatory states in uneven spatial distributions; (iii) The combined influence of diffusion rate and delay results in irregular oscillations in both time and space. However, we also find that fractional-order can suppress the formation of spatiotemporal patterns. These findings are crucial for comprehending the impact of network structure on the dynamics of fractional-order systems.

INDISPENSABLE DELAY ANALYSIS METHOD SELECTION: AN INTEGRATED AHP-RII APPROACH

A delay is an inevitable event in construction projects due to their intricacy and dynamic nature, therefore, the project parties frequently come to disputes about which party is liable for schedule delays. Delay analysis methods are gradually evolved and are used to steadily determine and apportion delay’s responsibilities. Notwithstanding, courts and arbitrary boards have not legalized any specific standard category of delay analysis technique to timely evaluate the impact of activity(es) delay on to total project duration. Also, there is no universal delay analysis method that can be used better than the others under certain circumstances. This research, thus, targets to seek the most appropriate and fundamental DAM for delay claims resolution. The study methodology adopted in the research by conducting an extensive literature review to identify current delay criteria which have much more impact on the selection of delay analysis method than existing delay analysis method is also carried out for an efficient discussion with a panel of experts. An Integrated Analytical Hierarchy Process and Relative Importance Index (AHP-RII) approach is ideally employed to analyze the highest and the most priority delay analysis method along with the critical factor weight influencing detecting the most proper type of delay analysis. Study findings are very authentic by fairly selecting the best delay analysis method based on reliable delay factors and available delay analysis methods. The researchers and practitioners may yield viable study outputs for incipiently practical preparation of a variety of data and documents which can be publicly supported as active and valid evidence for future claims.

Propagation Delay and Power Dissipation Analysis for a 2-Bit SRAM Using Multi-State SWS Inverter

This paper presents a study on the propagation delay and power dissipation of a 2-bit static random-access memory (SRAM) with two cross-coupled multi-state spatial wave function switching (SWS) CMOS inverters. The proposed SRAM design utilizes the advantages of the CMOS-SWS inverter, such as its small area, low power consumption, and high speed. The 2-bit SRAM circuit simulations were carried out in Cadence to analyze the power dissipation and propagation delay. An Analog Behavioral Model (ABM) and the Berkeley Short-channel IGFET Model (BSIM4.6) in 0.18-μm technology were combined to create this model. The analysis of the propagation delay shows that the multi-state CMOS-SWS SRAM significantly reduces the delay compared to other multi-state 6T SRAM memories. Additionally, the analysis of the power dissipation shows that the multi-state SWS-SRAM is comparable to conventional SRAMs. These results demonstrate the potential of multi-state SWS-SRAM for improving the performance of memory circuits and provide valuable insights for future design optimization.

Validation of octane hyperboosting phenomenon in prenol and structurally related olefinic alcohols

Hyperboosting is a recently discovered phenomenon in which the research octane number (RON) of a blend is higher than both the neat blendstock and the neat fuel it was blended into. RON is a measure of a fuel’s resistance to knock, and knock is a cause of engine inefficiency. Blends which exhibit hyperboosting are blends in which an overall improvement in engine efficiency may be expected. The first discovery of hyperboosting came from blending experiments in which prenol was blended into several different base fuels. Here, ignition delay time (IDT) measurements taken using a commercially available constant volume combustion chamber called the Advanced Fuel Ignition Delay Analyzer (AFIDA) are presented. The data show that some prenol blends have longer IDTs (lower reactivity) than either neat prenol or the base fuel, providing further evidence of hyperboosting. Additionally, more blending data is presented in which the base fuel is varied, which allows for a better understanding of hyperboosting sensitivity to chemical classes. The data indicate that aromatics may inhibit, and branched alkanes may enhance the magnitude of hyperboosting observed. Enthalpy of vaporization estimates are also given for several molecules which are blended into a 4-component surrogate. These estimates are derived from Equation of State simulations and reveal that there is no observable correlation between hyperboosting and enthalpy of vaporization. Blending data for molecules which share structural similarities with prenol are also presented. Structure property relationships are suggested, in which the double bond motif of prenol seems to play an important role in hyperboosting. This may help to understand hyperboosting and its underlying mechanism. Lastly, blending curves of surrogate blends with prenol experienced hyperboosting under lean (Homogeneous Charged Compression Ignition-HCCI) operating conditions, which validates that hyperboosting is not an artifact of the octane test methods, but inherent to the properties of prenol.

Towards the Designing of Low-Latency SAGIN: Ground-to-UAV Communications over Interference Channel

We present a novel and first-of-its-kind information-theoretic framework for the key design consideration and implementation of a ground-to-unmanned Aerial Vehicle (UAV) (G2U) communication network with an aim to minimize end-to-end transmission delay in the presence of interference in Space-Air-Ground Integrated Networks (SAGIN). To characterize the transmission delay, we utilize Fano’s inequality and derive the tight upper bound for the capacity for the G2U uplink channel in the presence of interference, noise, and potential jamming. In addition, as a function of the location information of the UAV, a tight lower bound on the transmit power is obtained subject to the reliability constraint and the maximum delay threshold. Furthermore, a relay UAV in the dual-hop relay mode, with amplify-and-forward (AF) protocol, is considered, for which we jointly obtain the optimal positions of the relay and the receiver UAVs in the presence of interference, with straight-line, circular, and helical trajectories as UAV tracing. Interestingly, increasing the power gives a negligible gain in terms of delay minimization, though may greatly enhance the outage performance. Moreover, we prove that there exists an optimal height that minimizes the end-to-end transmission delay in the presence of interference. We show the interesting result of the delay analysis. In particular, it is shown that receiver location and the end-to-end signal-to-noise power ratio play a critical role in end-to-end latency. For instance, with the transmitter location fixed to (0, 0, 0) and the interferer location set to (0, 500 m, 0), the latency generally increases with increasing the receiver’s vertical height (z-axis). With the receiver’s horizontal coordinates, i.e., (xR, yR) set to (0, 0) reducing the receiver’s height from 200 m to 50 m decreases the delay latency (codeword length) by more than 30% for an interference-limited channel. Whereas, for an interference channel with a signal-to-noise power ratio equal to 30 dB, the latency decreases by approximately 2%. The proposed framework can be used in practice by a network controller as a system parameters selection criteria, where among a set of parameters, the parameters leading to the lowest transmission latency can be incorporated into the transmission. The based analysis further set the baseline assessment when applying Command and Control (C2) standards to mission-critical G2U and UAV-to-UAV (U2U) services.

Spheroidal-wave analysis of time delay in molecular reconstruction of attosecond beating by interference of two-photon transitions around a Cooper-like minimum

Spheroidal-wave analysis of time delay in molecular reconstruction of attosecond beating by interference of two-photon transitions around a Cooper-like minimum

Rotor equivalent wind speed prediction based on mechanism analysis and residual correction using Lidar measurements

Efficient power generation and reduced operational cost are crucial to make wind energy a successful market player. To achieve satisfactory performance in wind turbine technology, it is essential to accurately predict the rotor equivalent wind speed (REWS). Since traditional wind transducers are difficult to measure multiple-point wind speed, related researches on predicting REWS are lacking. In this paper, a hybrid REWS prediction framework is proposed, which combines mechanism analysis and data-driven technique, aided by Lidar measurements. The framework employs the mechanism modeling and time delay analysis to achieve preliminary REWS prediction, but the accuracy is restricted by the quantity of Lidar measurement points. To enhance the prediction accuracy, a residual prediction model based on the data-driven technique is created to correct the mechanism prediction. Specifically, a novel combined modeling approach based on empirical mode decomposition and sample entropy algorithms is presented. Through reconstructing the input data of the combined model into high, middle, and low frequency components and exploring the prediction laws of different frequency components obtained by typical machine learning models, the combined residual prediction model is established. Additionally, an enhanced whale optimization algorithm named WOAmM is presented to optimize the parameters of the combined model. The proposed method is validated through a four-beam Lidar measurement scenario simulated by BLADED software. The experimental results indicate that the combined model outperforms each single model and the mechanism prediction, and improves the REWS prediction accuracy by up to75.04%, 60.92%, 51.85%, and 57.52% in the four datasets, respectively. The proposed method could facilitate the utilization of Lidar in improving the performance of wind turbines.

Theoretical analysis of time delay echoes in multimode semiconductor diode laser with optical feedback

Theoretical analysis of time delay echoes in multimode semiconductor diode laser with optical feedback

MSCET: A Multi-Scenario Offloading Schedule for Biomedical Data Processing and Analysis in Cloud-Edge-Terminal Collaborative Vehicular Networks.

With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoTs), an increasing number of computation intensive or delay sensitive biomedical data processing and analysis tasks are produced in vehicles, bringing more and more challenges to the biometric monitoring of drivers. Edge computing is a new paradigm to solve these challenges by offloading tasks from the resource-limited vehicles to Edge Servers (ESs) in Road Side Units (RSUs). However, most of the traditional offloading schedules for vehicular networks concentrate on the edge, while some tasks may be too complex for ESs to process. To this end, we consider a collaborative vehicular network in which the cloud, edge and terminal can cooperate with each other to accomplish the tasks. The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge. We further construct the virtual resource pool which can integrate the resource of multiple ESs since some regions may be covered by multiple RSUs. In this paper, we propose a Multi-Scenario offloading schedule for biomedical data processing and analysis in Cloud-Edge-Terminal collaborative vehicular networks called MSCET. The parameters of the proposed MSCET are optimized to maximize the system utility. We also conduct extensive simulations to evaluate the proposed MSCET and the results illustrate that MSCET outperforms other existing schedules.

The impact of vehicle traffic volume on pedestrian behavior at unsignalized crosswalks

The research is focused on analyses of vehicle delays caused by pedestrian crosswalks on one-way streets. The study encompassed three particular locations of unsignalized crosswalks: on the street section between intersections, in the zone of intersection impact, and in the zone of intersection itself. The study revealed that the major impact on traffic jams is observed at a distance of 50-100 m from the unsignalized crosswalk. The study analyses pedestrian behavior and waiting time at such crosswalks. It was confirmed that the pedestrians did not immediately use their priority before passing. The paper presents the change in pedestrian waiting time when crossing the roadway depending on traffic flow. Based on that data, a model was prepared and simulations of pedestrian behavior at unsignalized crosswalks were performed.

Risk Analysis of Pemalang District Road and Bridge Construction Delays

Risk Analysis of Pemalang District Road and Bridge Construction Delays

TOTAL FLOAT MANAGEMENT TECHNIQUE UNDER THE FLOAT AND RESOURCE LOADING

Schedule delays in construction projects are a common occurrence and have a significant impact on the overall project duration. Plentiful delay analysis techniques have been proposed and utilized to resolve these issues.The total float management method is widely accepted and considered the most fundamental approach as it effectively resolves four major delay issues and measures float consumption. However, current delay analysis techniques have often overlooked the allocation of float and resources, which can remarkably affect the determination of delay responsibilities for involved parties. This study aims to enhance the total float management approach by intensively incorporating float and resource loading considerations before delayanalysis implementation. The research methodology includes an extensive literature review, identification of crucial issues related to schedule delays arising from the lack of resource and total float allocation, and the application of enhanced total float management under float and resource loading through a case study for test validation. The research findings provide transparent insights into delay liability considering both time delays and float consumption and emphasize the importance of float and resource allocation in the early stages of construction projects for a consensus before analyzing and assigning delay responsibilities. Future studies could focus on legalizing the procedural considerations of float ownership and resource allocation in contractagreements.

Departure delay prediction and analysis based on node sequence data of ground support services for transit flights

Delays frequently occur during ground support services for transit flights. Due to the limitation of related data acquisition, the delay occurred during this process has seldom been explored. Toward the purpose of departure delay mitigation, the present research provides the node sequence data to identify the critical ground support service nodes that associate with departure delay. Machine learning techniques are used for accurate departure delay prediction of transit flights. First, the topological structure of the neural network is constructed according to the ground service support procedure of transit flights. The flight data from Shanghai Pudong and Kunming Changshui International airports are collected for delay description and model evaluation. Then, similarity of delay scenarios are illustrated and the typical delay patterns are explored based on clustering methods. It is found that there are some node sections that delays routinely occur. Third, a Bayesian network is constructed for delay probability analysis. The conditional probability table describing the impact of delay occurred during each section on the departure delay for transit flights is provided. Finally, the performance of two classes of approaches to predict departure delays are constructed and compared, including three traditional machining learning methods denoted as random forest, decision tree, XGBoost models, and four graph convolutional neural network models namely Graph convolutional network (GCN), Graph attention network (GAT), Graph sample and aggregate (GraphSAGE) and integrated Graph convolutional network and Graph sample and aggregate (GCN-GraphSAGE) models. In general, the proposed GCN-GraphSAGE method achieves the most stable and satisfactory performance for departure delay prediction. The research results can support the dynamic and accurate prediction of departure delays and the refined management of transit flights for delay alleviation.

Delays Analysis Of TRANSKO Tawes 11.3 DWT Mooring Boat Development Project Based On Risk Management

Delays Analysis Of TRANSKO Tawes 11.3 DWT Mooring Boat Development Project Based On Risk Management