Arrival Time Research Articles

DCE-Qnet: deep network quantification of dynamic contrast enhanced (DCE) MRI.

Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption. A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial input function. Network training incorporated B1 inhomogeneities to estimate perfusion (Ktrans, vp, ve), tissue T1 relaxation, proton density and bolus arrival time (BAT). The accuracy was tested in a digital phantom in comparison to a conventional nonlinear least-squares fitting (NLSQ). In vivo testing was conducted in ten healthy subjects. Regions of interest in the cervix and uterine myometrium were used to calculate the inter-subject variability. The clinical utility was demonstrated on a cervical cancer patient. Test-retest experiments were used to assess reproducibility of the parameter maps in the tumor. The DCE-Qnet reconstruction outperformed NLSQ in the phantom. The coefficient of variation (CV) in the healthy cervix varied between 5 and 51% depending on the parameter. Parameter values in the tumor agreed with previous studies despite differences in methodology. The CV in the tumor varied between 1 and 47%. The proposed approach provides comprehensive DCE-MRI quantification from a single acquisition. DCE-Qnet eliminates the need for separate T1 scan or BAT processing, leading to a reduction of 10 min per scan and more accurate quantification.

Multimodal urban mobility solutions for a smart campus using artificial neural networks for route determination and an algorithm for arrival time prediction

Artificial intelligence algorithms play a key role in solving multimodal urban mobility problems, this study outlines a scheme covering bus, boat, and pedestrian transport modes. This involves designing an Artificial Neural Network (ANN) model to classify the most suitable routes for passengers, together with an algorithm capable of estimating the arrival times of various transportation modes at designated stopping points or buildings within the smart campus. The information used to train the ANN is obtained from an Internet of Things (IoT) network and a database that includes the available routes within the campus (which is situated in the Brazilian Amazon), where there is a multimodal electric mobility service. In seeking to achieve the objectives, this ANN relied on the user's geographic location in the input layer, and route mapping data stored in the database in the output layer, as well as the backpropagation algorithm for computational processing and adjustments of synaptic weight. The algorithm for modal arrival time prediction used real-time geographic location data, together with the Haversine formula to calculate geographic distance, while the average speed was obtained from the IoT network. A range of different ANN parameters were included in the experiments. In the study case, the ANN results show an improvement of above 92% for determining the best routes and predicting arrival times. The findings show that ANNs can effectively find the best route within a real-world smart campus environment. Moreover, the results show the arrival time can be estimated by using real-time geographic location data.

Optimization method for low-velocity impact identification in nanocomposite using genetic algorithm

Abstract Fiber-reinforced plastic (FRP) is prone to invisible damage caused by low-velocity impact (LVI) during service. The structural health monitoring system is of great significance for damage monitoring and maintenance of composite materials. In this study, four fiber Bragg grating sensors were employed to collect the time domain strain signals of composite materials subjected to LVIs. Furthermore, a numerical simulation model was established to rapidly obtain impact signal dataset. The signal arrival time, peak time, and peak amplitude were selected as signal features, and the backpropagation neural network was successfully applied to determine the location and energy of LVIs. To address the issue of peak feature extraction in the strain signal processing, a genetic algorithm-based sliding window peak detection optimization method was proposed, which significantly improved the final prediction accuracy. The experimental results indicated that within a position range of 300 mm × 300 mm, the average positioning error can reach 5.1 mm; and in an energy range of 0.5–1 J, the average energy prediction error can reach 0.030 J. The proposed method achieved accurate identification of the LVI location and energy for FRP.

Characteristics analysis of flame propagation and its coupling effect with other parameters in LPG pipeline

AbstractTo study the flame propagating characteristic and its coupling effect with other parameters in the LPG pipeline, a typical pipeline model with two equal‐length branches perpendicular to each other is designed for experiment and simulation. Then, gas explosion scenarios are experimentally tested and numerically simulated, which is followed by the analysis of flame shape changing with time and peak temperature changing with space. Results show that when passing through the bifurcation, flame propagates to vertical branch B in a sharp knife shape affected by the strong vortex, reflected airflow, and compressed pressure wave in the pipeline with a diameter of 0.125 m. At the monitoring point that is 0.4 m away from the bifurcation point, the peak temperature of the vertical branch B is 57.87% bigger than that of the horizontal branch C, and its arrival time is 80% longer than that of the horizontal branch C, due to the existence of flame in vertical branch B. What's more, in both branches, the coupling results between peak temperature and peak velocity agree very well with the growth function, while the coupling results between peak temperature and peak pressure agree well with the decay function, providing aids to the optimal layout design of industrial pipeline branches as well as to the explosion suppression measures.

Bright and early: artificial light affects arrival time, but not group size or vigilance in Little Penguins (Eudyptula minor)

Coastal developments are ever‐expanding and increasing the use of artificial lights within marine environments. Yet there is conflicting research on the impact of artificial lights on seabirds. Here, we experimentally investigated the impacts of artificial white lights on the behaviours (arrival time, group size, number of groups and vigilance) of breeding Little Penguins Eudyptula minor. Little Penguins are central‐place foragers that spend daylight hours foraging at sea and return to their breeding colony after sunset to attend to their chicks or relieve their incubating partners. We exposed Little Penguins returning to their colony at night to either (1) a self‐sustaining white LED floodlight or (2) a control system with a decoy light turned ‘off’. We used two different landing sites (site 1, site 2) that differed in landscape characteristics to assess whether behavioural responses to light were site‐specific. Little Penguins arrived in larger groups at the landing site 2. Regardless of site, we observed fewer groups that arrived earlier when the light was ‘on’. The effects of artificial light (or ‘lack of artificial light’) on the vigilance of Little Penguins were site‐specific, with Little Penguins spending proportionally more time in vigilance when the light was ‘off’ at site 2 compared with site 1. Our results support the idea that artificial lights produced from coastal developments can alter penguin behaviours, but that the effects of artificial lights can be context‐dependent and need to be assessed on a case‐by‐case basis.

Optimizing integrated berth allocation and quay crane assignment: A distributionally robust approach

In this research, we have formulated a Two-Stage Distributionally Robust Optimization (TDRO) model within the context of a mean–variance ambiguity set, specifically designed to address the challenges in the Integrated Berth Allocation and Quay Crane Assignment Problem (BACAP). A key consideration in this study is the inherent uncertainty associated with ships’ arrival times. During the initial stage, we derive a baseline schedule governing berth allocation and quay crane assignment. Anticipating potential disruptions arising from uncertain arrival delays, the second stage is meticulously formulated to determine the worst-case expectation of adjustment costs within the mean–variance ambiguity set. Subsequently, we undertake an equivalent transformation, converting the general TDRO model into a Two-Stage Robust Second-Order Cone Programming (TRO-SOCP) model. This transformation facilitates the application of the Column and Constraint Generation (C&CG) algorithm, ensuring the derivation of an exact solution. To address the computational intricacies associated with second-order cone programming, we propose two enhancement strategies for upper and lower bounds, aimed at expediting the solution process. Additionally, to contend with large-scale instances, we introduce a refinement and approximation method, transforming the TDRO model into a Mixed-Integer Programming (MIP) model. Furthermore, extensive numerical experiments are executed on both synthetic and real-life instances to validate the superior performance of our model and algorithms. In terms of the total cost, the TDRO model demonstrates superior performance compared with Two-Stage Stochastic Programming (TSP) and Two-Stage Robust Optimization (TRO) models.

Differentiation of regioisomers of sulfobenzoic acid by traveling-wave ion mobility mass spectrometry.

An ion mobility mass spectrometry (IM-MS) investigation using a Synapt G2 mass spectrometer was conducted to separate anions generated from the three regioisomers of sulfobenzoic acid. The results revealed that the differences in arrival time distributions (ATDs) were inadequate to differentiate the isomers unambiguously. However, the ATD profiles of the product ions, generated by fragmenting the respective mass-selected m/z 201 precursor ions in the Trap region of the three-compartment traveling-wave ion guide of the Synapt G2 mass spectrometer, were distinctly different, enabling definitive differentiation of the isomers. An arrival-time peak for an ion of m/z 157 resulting from the loss of CO2 from the respective precursors was common to all three mobilograms. However, only the profile recorded from the para-isomer exhibited a unique arrival-time peak for an ion of m/z 137, originating from an SO2 loss. Such a peak corresponding to an SO2 loss was absent in the ATD profiles of the ortho- and meta-isomers. Additionally, the mobilogram of the meta-isomer displayed a unique peak at 3.42 ms. Based on its product ion spectrum, this peak was attributed to the bisulfite anion (m/z 81; HSO3-). Previously, this meta-isomer specific m/z 81 ion had been proposed to originate from a two-step process involving the intermediacy of an m/z 157 ion formed by CO2 loss. However, our detailed tandem mass spectrometric experiments suggest that the m/z 81 is not a secondary product but rather an ion that originated from a direct elimination of a benzyne derivative from the m/z 201 precursor ion.

Readout studies for the HIKE main electromagnetic calorimeter

The High-Intensity Kaon Experiment (HIKE) is a proposed future kaon experiment designed for the CERN SPS accelerator. To cope with a planned increase of beam intensity by a factor of 4 with respect to the current NA62 experiment, the new Main Electromagnetic Calorimeter (MEC) has to provide a similar improvement in time resolution over the current Liquid Krypton calorimeter, down to ≈100ps for a 40GeV photon, while maintaining a comparable energy and spatial resolution. To achieve these goals, the planned detector consists of a shashlik calorimeter paired with high-resolution analog to digital converter for both energy deposit and arrival time estimates, operating at fast sample rates (∼1GHz).This contribution describes a prototype of the readout system using commercial “off-the-shelf” boards. In particular, we focused on feature extraction and tested it using a pulse generator. We included a data readout logic based on the TCP/IP protocol allows for easy deployment in beam tests.

Wireless sensor localization based on distance optimization and assistance by mobile anchor nodes: a novel algorithm

Wireless sensor networks (WSNs) have wide applications in healthcare, environmental monitoring, and target tracking, relying on sensor nodes that are joined cooperatively. The research investigates localization algorithms for both target and node in WSNs to enhance accuracy. An innovative localization algorithm characterized as an asynchronous time-of-arrival (TOA) target is proposed by implementing a differential evolution algorithm. Unlike available approaches, the proposed algorithm employs the least squares criterion to represent signal-sending time as a function of the target position. The target node’s coordinates are estimated by utilizing a differential evolution algorithm with reverse learning and adaptive redirection. A hybrid received signal strength (RSS)-TOA target localization algorithm is introduced, addressing the challenge of unknown transmission parameters. This algorithm simultaneously estimates transmitted power, path loss index, and target position by employing the RSS and TOA measurements. These proposed algorithms improve the accuracy and efficiency of wireless sensor localization, boosting performance in various WSN applications.

Experimental Study of a Temperature Field in a Shut-in Well in Relation to Determining Behind-the-Casing Flow Using Active Thermometry

Abstract —This paper presents results of experimental studies of a thermal field in the barrel of a shut-in (no fluid movement in the casing) well in relation to determining a behind-the-casing upward flow using the method of active thermometry. The studies are carried out using the physical model of a well that is a vertically located steel pipe with a system of externally attached copper tubes simulating a behind-the-casing flow. The pipe contains a local heating section, above which a temperature probe is located to record thermal disturbance from the heating section. The effect of free convection in a fluid on the temperature field in the pipe during and after heating is described. It is revealed that there are high-frequency temperature oscillations on sensors that record the temperature of the inner surface (wall) of the pipe and fluid above the heating area, whose value reaches higher than 2 °C and decreases when the distance to the heating region becomes longer. There is an empirical relationship that relates the time of arrival of the temperature disturbance front associated with free convection and the distance to the pipe heating region. Azimuthal temperature distribution curves on the inner wall of the pipe above the heating section are constructed in the absence and presence of a behind-the-casing flow. Qualitative criteria have been obtained indicating the presence of an azimuthally localized behind-the-casing flow (sector flow) of fluid based on the azimuthal temperature distribution analysis.

Local social perception of mining in Parauapebas and Canaã dos Carajás in the eastern Amazonia, Brazil

The mineral industry plays a role in structural transformations in southeastern Pará, Eastern Amazonia, where large projects in this sector are located. This study analyzes aspects of the social perception of mining from image and reputation concepts in the Amazonian context. This paper aimed to examine the role of socioeconomic, political, and historical variables on social perception. It focused on four dimensions - economic, environmental, relationship and image, and general dimensions in Parauapebas and Canaã dos Carajás cities. The methodology consisted of the application in April of 2018 of questionnaires in a stratified sample of 960 and 349 randomly selected households in each of those cities, respectively. We employed the PCA/factor analysis method to examine whether a set of variables is relevant to social perception. The study found that the mining industry is a partner – not an intruder – because its beginning coincided with the social and historical formation of the territory. Perceptions of image and reputation were positive. Interviewee education, age, income, Whatsapp and Facebook usage, having a formal job, belonging to a union and a political party, occupation, and time of arrival are the main factors for social perception of mining. Despite their significantly negative opinion concerning mining's environmental impact, most interviewees pointed out that mining is reliable, friendly, transparent, and involved with the municipalities' problems. Our work fills a gap in growing literature that shows the challenges of companies, governments, and civil society in contemplating the dynamic aspects of mining for effective planning aiming at the social sustainability of mining regions.

Acoustic emission source localization in complex pipe structure using multi-task deep learning models

Localizing defects in long-range pipelines is essential to reduce the inspection time and develop timely repair strategies. The acoustic emission (AE) method is employed to pinpoint the position of defects in pipelines. The conventional 1-D localization algorithm requires time of arrival differences between two sensors, which may not be accurately captured due to the dispersive nature of the pipe structures. The geometric variations such as elbows and welds can influence the propagating elastic waves and, consequently, arrival time. In this study, an AE source localization approach using a deep learning model is developed to tackle the influences of sensor-source distance and geometric variables. The multi-task learning model first identifies the impact of the elbow and subsequently integrates this information when predicting the source location. The proposed model is evaluated on a complex piping system, which features welded elbows in its connections. Incorporating the elbow effect into the model shows a notable improvement in overall accuracy, rising from 53% (conventional method) to 94% (proposed multi-task learning method).

Hybrid PDES Simulation of HPC Networks Using Zombie Packets

Although high-fidelity network simulations have proven to be reliable and cost-effective tools to peer into architectural questions for high-performance computing (HPC) networks, they incur a high resource cost. The time spent in simulating a single millisecond of network traffic in the highest detail can take hours, even for static, well-behaved traffic patterns such as uniform random. Surrogate models offer a significant reduction in runtime, yet they cannot serve as complete replacements and should only be used when appropriate. Thus, there is a need for hybrid modeling, where high-fidelity simulation and surrogates run side-by-side. We present a surrogate model for HPC networks in which: packets bypass the network, while the network state is left untouched, i.e , suspended. To bypass the network, we use historical data to estimate the arrival time at which every packet should be scheduled at; to suspend the network, all in-flight packets are scheduled to arrive at their destinations, and are kept in the system to awaken as zombies when switching back to high-fidelity. Speedup for a hybrid model is relative to the proportion of surrogate to high-fidelity. This light-weight surrogate obtained up to 76 × speedup. Keeping the zombies in the network showed an increase in the accuracy of the high-fidelity simulation on restart when compared to restarting the network from an empty state.

Designing Improvement of Service effectiveness Using M/M/1 and M/M/S Queue Models (Case Study: Customer Arrivals at Jatim Park Tourist Destination During Holidays and Peak Hours)

Queueing is a collection of people lined up with the purpose of waiting to be served, after which they leave the queue. Queuing activities often arise due to ineffective or suboptimal service times, resulting in longer wait times. To maximize and schedule service time optimally, queueing models such as M/M/1 and M/M/S systems are utilized. This study applies these models to analyze the queuing system at Jatim Park tourist destination. The aim of this research is to determine the effectiveness of service at the ticketing counters of Jatim Park during holidays. To initiate the study, data was collected on visitor arrival times and average service times. The data analysis aims to optimize service effectiveness at the Kampung Coklat tourist area by implementing the FCFS (First Come, First Served) queueing method and the M/M/S system. The findings suggest that the optimal number of teller servers needed to serve visitors effectively is 2. The analysis also identifies that the longest queue occurs between 10:00 AM and 11:00 AM, with 3 visitors waiting at its peak. The average number of visitors in the queue during this period is approximately 1.3 or 1 visitor. Based on these results, the study concludes that the service effectiveness at the Kampung Coklat tourist area is sufficiently optimized.

Characteristics, management and outcome of patients with late-arrival STEMI in the Acute Coronary Syndrome Israeli Surveys (ACSIS)

IntroductionPatients with ST-elevation myocardial infarction (STEMI) and late arrival (>12 h) after symptom onset, are at high risk for mortality and heart failure and represent a challenge for management. We aimed to define patient characteristics, management, and outcome of late-arrival STEMI in Israel over the last 20 years. MethodsWe analyzed data of late-arrival STEMI (12–48 h and > 48 h) from the biennial acute coronary syndrome Israeli Surveys (ACSIS), as well as time-dependent changes [early (2000–2010) Vs. late (2013–2021) period]. ResultsData regarding time from symptom onset to hospital arrival was available in 6,466 STEMI patients. Of these, 9.6 % arrived 12–48 h and 3 % >48 h from symptom onset. Late-arrival patients were more likely to be older women with diabetes and high GRACE score and less likely to have prior myocardial infarction.In recent years, 95 % of patients arriving 12–48 h and 96 % of those arriving > 48 h had coronary angiography, as opposed to 75 % and 77 % in the early years (p = 0.007). Percutaneous coronary intervention (PCI) increased from 60 % and 55 % respectively to 85 % (p ≤ 0.001).TIMI-3 flow after primary PCI was 89–92 %, irrespective of arrival time. Late arrival patients (12–48 h but not > 48 h) who had PCI had better adjusted 1-year survival, HR 0.49 (95 %CI 0.29–0.82), p = 0.01. ConclusionsLate-arrival STEMI patients have higher risk characteristics. Most late-arrival patients undergo coronary angiography and PCI and have TIMI-3 flow after primary PCI. In patients arriving 12–48 h after symptom onset PCI is associated with better survival.

Molecular Dynamics‐Based Conformational Simulation Method for Analysis of Arrival Time Distributions in Ion Mobility Mass Spectrometry

Molecular Dynamics‐Based Conformational Simulation Method for Analysis of Arrival Time Distributions in Ion Mobility Mass Spectrometry

Evidence of low velocity layers at the top and bottom of the Mantle Transition Zone (MTZ) below the Uttarakhand Himalaya, India

The Mantle Transition Zone (MTZ) beneath the Uttarakhand Himalaya has been modelled using Common Conversion Point (CCP) stacking and depth-migration of radial P-receiver functions. In the Uttarakhand Himalaya region, the depths of the 410-km discontinuity (d410) and the 660-km discontinuity (d660) are estimated to be approximately 406 ± 8 km and 659 ± 10 km, respectively. Additionally, the thickness of the mantle transition zone (MTZ) is modelled to be 255 ± 7 km. The average arrival times for d410 and d660 conversions are (44.47 ± 1.33) s and (71.08 ± 1.29) s, respectively, indicating an undisturbed slightly deeper d410 and a deformed noticeably deeper d660 in the area. The model identifies the characteristics of the d410 and d660 mantle discontinuities beneath the Lesser Himalayan region, revealing a thickening of the MTZ towards northeast, which could be due to gradual cooling or thickening of the Indian lithosphere towards its northern limit. We simulate a low-velocity layer (perhaps partially molten) above the d410 discontinuity at depths of 350 to 385 km, indicating the presence of a hydrated MTZ beneath the area. We also interpret a negative phase at d660 as a low-velocity layer between 590 and 640 km depths, which could be attributed to the accumulation of old subducted oceanic materials or increased water content at the bottom of the MTZ. Our results suggest the presence of residues from paleo-subducted lithospheric slabs in and below the mantle transition zone underlying the Uttarakhand Himalayas.

High Resolution Ion Mobility Enables the Structural Characterization of Atropisomers of GDC-6036, a KRAS G12C Covalent Inhibitor.

GDC-6036 is a covalent KRAS G12C inhibitor that demonstrates high potency and selectivity. Structurally, GDC-6036 consists of several motifs that make the analytical characterization of this molecule challenging, including a highly basic pyrrolidine motif bonded to a quinazoline ring via an ether bond and an atropisomeric carbon-carbon bond between functionalized pyridine and quinazoline groups. Structurally, the desired atropisomer was synthesized via an atroposelective Negishi coupling with very high yield. However, having a direct way to analyze and confirm the presence of the atropisomeric species remained challenging in routine analytical workflows. In this study, both variable temperature nuclear magnetic resonance (VT-NMR) and two different approaches of in-line ion mobility coupled to liquid chromatography mass spectrometry (LC-MS) workflows were evaluated for the characterization of GDC-6036 and its undesired atropisomer (Compound B) to support synthetic route development. Briefly, both VT-NMR and traveling wave ion mobility spectrometry (TWIMS) enabled by structures for lossless ion manipulation (SLIM) technology coupled to high resolution MS (HRMS) are able to elucidate the structures of the atropisomers in a complex mixture. Drift tube IMS (DTIMS) was also evaluated, but lacked the resolving power to demonstrate separation between the two species in a mixture, but did show slight differences in their arrival times when multiplexed and injected separately. The determined resolving power (Rp) by multiplexing the ions via DTIMS was 67.3 and 60.5 for GDC-6036 and Compound B, respectively, while the two peak resolving power (Rpp) was determined to be 0.41, indicating inadequate resolution between the two species. Alternatively, the SLIM-IM studies showed Rp of 103.8 and 99.4, with a Rpp of 2.64, indicating good separation between the atropisomers. Furthermore, the CCS/z for GDC-6036 and Compound B was determined to be 231.2 Å2/z and 235.0 Å2/z, respectively. Quantitative experiments demonstrate linearity (R2 >0.99) for both GDC-6036 and Compound B while maintaining separation via SLIM-IM. Spike recoveries of one atropisomer relative to the other yielded strong recoveries (98.7% to 102.5%) while maintaining reproducibility (<7% RSD). The study herein describes the analytical process for evaluating new technologies and strategies for implementation in routine biopharmaceutical characterization workflows.

A Simple Scoring System for Identifying Favorable Neurologic Outcomes Among Out-of-Hospital Cardiac Arrest Patients With Asystole

Study objectiveAsystole is the most common initial rhythm in out-of-hospital cardiac arrest (OHCA), but indicates a low likelihood of neurological recovery. This study aimed to develop a novel scoring system to be easily applied at the time of emergency department arrival for identifying favorable neurological outcomes in OHCA survivors with an asystole rhythm. MethodsThis study is a secondary analysis based on a previously collected nationwide database, targeting non-traumatic adult OHCA patients aged ≥ 18 years with an asystole rhythm who achieved Return of Spontaneous Circulation (ROSC) between January 2016 and December 2020. The primary outcome was a favorable neurological outcome defined as Cerebral Performance Categories scores of 1 or 2 at hospital discharge. A prediction model was developed through multivariable logistic regression analysis in a derivation cohort in the form of a scoring system (WBC-ASystole). The performance and calibration of the model were tested using an internal validation cohort. ResultsAmong 19,803 OHCA patients with survival to hospital admission, 6,322 had asystole, and 285 (4.5%) achieved good neurological outcomes. Factors associated with favorable outcomes included age, witness arrest, bystander cardiopulmonary resuscitation, time from call to hospital arrival, and prehospital ROSC achievement. The WBC-ASystole score, totaling 11 points, exhibited a predictive performance with an area under the receiver operating characteristic curve of 0.80 (95% confidence interval [CI]: 0.76–0.83) and 0.79 (95% CI: 0.74–0.83) in the derivation and validation cohorts, respectively. After categorizing patients into three groups based on probability for good neurological outcomes, the sensitivity and specificity were as follows: 0.98 (95% CI: 0.97–0.99) and 0.09 (95% CI: 0.09–0.10) for the very low (WBC-ASystole: ≤2), 0.85 (95% CI: 0.82–0.89) and 0.54 (95% CI: 0.53–0.55) for the low (WBC-ASystole: 3–4), and 0.36 (95% CI: 0.34–0.39) and 0.93 (95% CI: 0.92–0.93) for fair predicted probability group (WBC-ASystole: ≥5). ConclusionsAlthough external validation studies must be performed, among OHCA with asystole, the WBC-ASystole scoring system may identify those patients who are likely to have a favorable neurological outcome.

Spatial digital twin framework for overheight vehicle warning and re-routing system

Overhead road obstacles present a significant logistical hazard to the heavy vehicle industry. Traditional overheight vehicle warning systems such as passive warning systems (PWS) and active warning systems (AWS) have not adequately reduced the frequency and impact of overheight incidents, encouraging transportation agencies to employ intelligent transport system (ITS) strategies using state-of-the-art advanced technologies. This research takes an innovative approach in developing an immersive user-focused experience, harnessing multi-disciplinary methods and tools to engineer a spatial digital twin prototype for a novel Internet-of-Things (IoT)-based active warning alert and re-routing system (AWARS). LiDAR and 3D GIS were used to model the complex road environment, tailored to the strict fiscal objectives sought by economically mindful organisations. Tree crowns were extracted from near-Infrared aerial imagery and digital elevation models, supplying the dimensions necessary for 3D tree modelling. IoT connectivity was configured using a real-time analytics approach to deliver alerts and re-routing options. The World Traffic Service with live and predictive traffic data was used for the routing application programming interface (API). A standard-configuration common rigid truck (CRT) was inserted into the 3D road environment model to simulate overheight collisions and to ascertain the effect of re-routing on estimated time of arrival (ETA). Longer ETA durations were observed for routes computed by the digital twin. Theoretically, enhanced situational awareness and subsequent reduction of risk likelihood suggests an optimised response to industry demands, despite extended travel times, cultivating a favorable impact on the supply chain through enhanced safety management.