Successful Deployment Research Articles

Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial

Accurate identification of genetic alterations in tumors, such as Fibroblast Growth Factor Receptor, is crucial for treating with targeted therapies; however, molecular testing can delay patient care due to the time and tissue required. Successful development, validation, and deployment of an AI-based, biomarker-detection algorithm could reduce screening cost and accelerate patient recruitment. Here, we develop a deep-learning algorithm using >3000 H&E-stained whole slide images from patients with advanced urothelial cancers, optimized for high sensitivity to avoid ruling out trial-eligible patients. The algorithm is validated on a dataset of 350 patients, achieving an area under the curve of 0.75, specificity of 31.8% at 88.7% sensitivity, and projected 28.7% reduction in molecular testing. We successfully deploy the system in a non-interventional study comprising 89 global study clinical sites and demonstrate its potential to prioritize/deprioritize molecular testing resources and provide substantial cost savings in the drug development and clinical settings.

The Amalgamation of Federated Learning and Explainable Artificial Intelligence for the Internet of Medical Things: A Review

Abstract: The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, integrating the Internet of Things (IoT) with medical devices, sensors, and healthcare systems. From peripheral devices that monitor vital signs to remote patient monitoring systems and smart hospitals, IoMT provides a vast array of applications that empower healthcare professionals. However, the integration of IoMT presents numerous obstacles, such as data security, privacy concerns, interoperability, scalability, and ethical considerations. For the successful integration and deployment of IoMT, addressing these obstacles is essential. Federated Learning (FL) permits collaborative model training while maintaining data privacy in distributed environments like IoMT. By incorporating Explainable Artificial Intelligence (XAI) techniques, the resulting models become more interpretable and transparent, enabling healthcare professionals to comprehend the underlying decision-making processes. This integration not only improves the credibility of Artificial Intelligence models but also facilitates the detection of biases, errors, and peculiar patterns in the data. The combination of FL and XAI contributes to the development of more privacy-preserving, trustworthy, and explainable AI systems, which are essential for the development of dependable and ethically sound IoMT applications. Hence, the aim of this paper is to conduct a literature review on the amalgamation of FL and XAI for IoMT.

A principal component analysis of sustainable building construction features for project delivery in South Africa

Purpose Successful project delivery for sustainable building construction (SBC) has been linked to certain features. Previous studies have emphasised the need to improve SBC practice in South Africa. The purpose of this study is to explore the SBC features for project delivery in South Africa. Design/methodology/approach A structured questionnaire elicited the primary data from 281 built environment professionals, mainly in South Africa’s Gauteng province. Descriptive and inferential statistics were used for the data analysis. This study used the principal component analysis technique to ascertain the principal SBC features. Findings Three components of SBC features, namely, sustainable resource use and compliance, sustainable waste minimisation and recycling and sustainable designs and materials, were developed from the principal component analysis. The factor loadings of the constituent variables ranged from 0.570 to 0.836. The reliability of each component was evaluated, and the results were 0.966, 0.931 and 0.913. Practical implications The revelations from this study will aid the decision-making of the relevant stakeholders towards establishing improvement initiatives and mitigating the reluctance to shift from conventional building methods and poor knowledge sharing of SBC benefits. Originality/value This is one of the most recent South African studies that sheds light on the components of a successful SBC deployment. The findings of this study added to knowledge by confirming three fundamental features of SBC. This study recommends adequately considering the principal features for successful SBC project delivery in South Africa.

Renewable Energy Technical Potential Performance for Zero Carbon Emissions.

Global 2021 energy flows indicate sectors with the greatest opportunities for efficiency improvements and emissions reduction. Analysis of the International Energy Agency's 2050 Net Zero Emissions (NZE) Scenario with respect to the 2021 energy profile as a reference point provides a model for changes in energy efficiency, emissions reduction, cost, and land use resulting from substitutions of fossil fuels with renewable options at the levels required for net zero emissions by 2050. Even with optimized outcomes, the probability of such widespread changes occurring over the next 30 years is reduced by the need for large investments, infrastructure changes, and the successful deployment of technologies still under development. Achievement of the technical potential outcomes for alternative solar energy to fuels conversion technologies, including cultivated microalgae biomass and biofuels and photocatalytic and photobiological hydrogen, would reduce reliance on mature renewable technologies and provide increase in net energy ("power to wheels") to help meet the demands of a growing population while reducing cost and CO2 emissions compared to the baseline 2050 NZE scenario. Such technical potential performance along with those for solar and wind represents 4 billion tons of CO2 removal (a 28% increase) over the baseline 2050 NZE Scenario but still requires another 9.5 billion tons of carbon capture and removal (CCR) to achieve NZE.

16-year outcomes of blunt thoracic aortic injury treated with thoracic endovascular aortic repair: A single-institution experience.

Blunt thoracic aortic injury (BTAI) is associated with considerable mortality and morbidity. Thoracic endovascular aortic repair (TEVAR) has essentially replaced open aortic repair (OAR) with superior outcome. The objective of this study was to evaluate the early and intermediate-term outcomes in patients sustaining BTAI treated with TEVAR, and to evaluate the prevalence and impact of left subclavian artery (LSA) coverage. This retrospective analysis includes patients undergoing TEVAR for BTAI between February 2006 and December 2022 at the Helsinki University Hospital, Finland. The primary endpoints were 30-day and 2-year mortality, technical success of stent-graft system deployment, and procedural and device-related complications. The secondary endpoints pertained to reintervention events: conversion to OAR, re-TEVAR, any endovascular/surgical reinterventions for optimal stent-graft function, or any reinterventions during follow-up. A total of 38 consecutive BTAI patients treated with TEVAR were included in the analyses. Median patient age was 45.5 years (range, 15-79) and 66% were male. The median follow-up period was 39 months. Technical success was 100%, 30-day mortality was 0%, and 2-year mortality was 11% in the study cohort. Coverage of LSA without revascularization (13/18) resulted in one postoperative nondisabling stroke (1/13), no paraplegia, and one had ischemic left arm findings (1/13). Only one patient required reintervention (LSA embolization; 1/38). In this institutional series, we provide further evidence in favor of TEVAR for BTAI treatment. We demonstrated that TEVAR is linked to highly favorable outcomes in the short and intermediate term, and coverage of LSA without revascularization was quite well tolerated.

Assessing the Suitability of Automation Using the Methods–Time–Measurement Basic System

Due to its high complexity and the varied assembly processes, hybrid assembly systems characterized by human–robot collaboration (HRC) are meaningful. Suitable use cases must be identified efficiently to ensure cost-effectiveness and successful deployment in the respective assembly systems. This paper presents a method for evaluating the potential of HRC to derive automation suitability based on existing or to-be-collected time data. This should enable a quick and favorable statement to be made about processes, for efficient application in potential analyses. The method is based on the Methods–Time–Measurement Basic System (MTM-1) procedure, widely used in the industry. This ensures good adaptability in an industrial context. It extends existing models and examines how much assembly activities and processes can be optimized by efficiently allocating between humans and robots. In the process model, the assembly processes are subdivided and analyzed with the help of the specified MTM motion time system. The suitability of the individual activities and sub-processes for automation are evaluated based on criteria derived from existing methods. Two four-field matrices were used to interpret and classify the analysis results. The process is assessed using an example product from electrolyzer production, which is currently mainly assembled by hand. To achieve high statement reliability, further work is required to classify the results comprehensively.

Endovascular Treatment of Complex Aortic Aneurysms Using Inner Branch Device (IBEVAR): A Single Center Experience

Abstract Background Due to advances in devices and techniques, the endovascular approach using fenestrated or branched endografts has emerged as a valid and safe alternative for patients with complex aortic aneurysms, especially if considered at high risk for surgery. The use of inner branch devices (iBEVAR) combining benefits of fenestrations and outer branches could offer an ideal configuration in this context. Aims The aim of our study was to report our monocentric experience with iBEVAR in the treatment of complex aortic aneurysms. Methods A retrospective analysis of prospective data retrieved from 1 center between January 2020 and January 2024 was done. Endpoints were immediate technical success, postoperative morbidity, rate of re-intervention and mortality. Results During this period, 72 patients with a mean age of 68.5 years (range 41-88) were identified. The maximum preoperative mean aortic diameter was 56 mm (44-71 mm). The majority of patients was asymptomatic. For diffuse thoraco-abdominal aortic aneurysms, a 2-steps procedure was used. There were 34 off-the-shelf E-nside (47%) and the remaining 38 patients were treated with a custom-made Extra-Design graft (53%). Despite successful endograft deployment in all patients and branch catheterization in 69/72 cases (96%), the technical success was 94% due to an additional open conversion for ilio-mesenteric bypass. The 30-days mortality was 5.5%. During the post-operative period, 19% of patients presented complications with 1 case of mesenteric ischaemia requiring a visceral resection and 2 cases of paralytic ileus, treated conservatively. 5 cases (6.9) of spinal cord ischemia were observed. During the median follow-up, 22 patients (31%) had a re-intervention, for endoleaks, bridging-stent events or limbs problems. The overall target vessel patency was 94%. Conclusion The use of iBEVAR appears to be safe and effective for the treatment of complex aortic aneurysms with an acceptable rate of complications. Strict follow-up is mandatory and about one-third of patients required a re-intervention. Further patients and longer follow-up are needed.

Loss and recovery of percutaneous femoral access during transcatheter aortic valve replacement. A case report

Loss and recovery of percutaneous femoral access during transcatheter aortic valve replacement. A case report

Evaluating Magnetic Seed Localization in Targeted Axillary Dissection for Node-Positive Early Breast Cancer Patients Receiving Neoadjuvant Systemic Therapy: A Comprehensive Review and Pooled Analysis.

Background/Objectives: De-escalation of axillary surgery is made possible by advancements in both neoadjuvant systemic therapy (NST) and in localisation technology for breast lesions. Magseed®, developed in 2013 by Dr. Michael Douk of Cambridge, United Kingdom, is a wire-free localisation technology that facilitates the localisation and retrieval of lymph nodes for staging. Targeted axillary dissection (TAD), which entails marked lymph node biopsy (MLNB) and sentinel lymph node biopsy (SLNB), has emerged as the preferred method to assess residual disease in post-NST node-positive patients. This systematic review and pooled analysis evaluate the performance of Magseed® in TAD. Methods: The search was carried out in PubMed and Google Scholar. An assessment of localisation, retrieval rates, concordance between MLNB and SLNB, and pathological complete response (pCR) in clinically node-positive patients post NST was undertaken. Results: Nine studies spanning 494 patients and 497 procedures were identified, with a 100% successful deployment rate, a 94.2% (468/497) [95% confidence interval (CI), 93.7-94.7] localisation rate, a 98.8% (491/497) retrieval rate, and a 68.8% (247/359) [95% CI 65.6-72.0] concordance rate. pCR was observed in 47.9% (220/459) ) [95% CI 43.3-52.6] of cases. Subgroup analysis of studies reporting the pathological status of MLNB and SLNB separately revealed an FNR of 4.2% for MLNB and 17.6% for SLNB (p = 0.0013). Mean duration of implantation was 37 days (range: 0-188). Conclusions: These findings highlight magnetic seed localisation's efficacy in TAD for NST-treated node-positive patients, aiding in accurate axillary pCR identification and safe de-escalation of axillary surgery in excellent responders.

Cellular automata-based MapReduce design: Migrating a big data processing model from Industry 4.0 to Industry 5.0

A successful deployment of Industry 5.0 is significantly dependent on the synergetic integration of several advanced technologies such as big data processing, Artificial Intelligence (AI) integration, and several effective digitization techniques that emphasize the uses of Robotics, Internet of Things (IoTs), Cloud Computing, etc. with active participations from human workers. Several researchers have explored the importance of big data processing in view of Industry 4.0 as it facilitated enhanced production at any smart manufacturing line by ensuring efficient process management, typically involving big data processing. Researchers presented several MapReduce-based data processing models at smart manufacturing lines to facilitate big data processing. Among several others, the Elementary Cellular Automata (ECAs)-based MapReduce model was introduced as an energy-efficient low-cost model for big data processing in view of the Industry 4.0 scenario. In the present research, an investigation is further proposed to explore the true potential (if any) for the ECAs-based MapReduce model with reference to available several MapReduce models, to migrate an existing big data processing model from Industry 4.0 into the future i.e., Industry 5.0. Investigation results as achieved from the comparison among several MapReduce models and further examinations about the inherent quality of shuffle in those MapReduce blocks, explore the inherent advantages of ECAs-based MapReduce model towards its choice for big data processing in Industry 5.0 scenario.

The impact of perceived cyber-risks on automated vehicle acceptance: Insights from a survey of participants from the United States, the United Kingdom, New Zealand, and Australia

No study has systematically investigated the public's perceptions of cyber risks and their relationship with the acceptance of fully Connected and Automated Vehicles (CAVs). To address this knowledge gap, we developed a conceptual model and investigated the impact of the cyber-emulated risks (cyberattack, safety risk, connectivity risk, privacy risk, and performance risk) that may influence the adoption of CAVs. We tested the proposed model using structural equation modelling with a nationally representative sample of 2062 adults from the US, UK, New Zealand, and Australia.The results indicate that perceived cyberattacks had a significant but marginally neutral effect on usage intent, illustrating the acceptance of technical risk with CAVs. This finding challenges the commonly held belief that cyberattacks negatively influence the adoption of products and technology in other product development fields, such as information technology. CAV cyberattacks elevate concerns about safety, connectivity, privacy, and performance risks. Interestingly, connectivity risk had no significant impact on CAV's behaviour intention, but mediation analysis showed it indirectly affects CAV's acceptance through privacy and performance risks. Regarding socio-demographic and technological attributes, participants of older age, middle income, low-middle education, high cybersecurity knowledge and AV understanding exhibit high anxiety about CAV cyberattacks. The results hold significant policy implications, suggesting the need for tailored strategies in enhancing the cybersecurity of CAVs to ensure their successful adoption and deployment. The findings of this study aim to enhance the quality of transport policy and bridge the gap between theory and practice in addressing cyber risks in the transport sector.

3D-printed, citrate-based bioresorbable vascular scaffolds for coronary artery angioplasty

Fully bioresorbable vascular scaffolds (BVSs) aim to overcome the limitations of metallic drug-eluting stents (DESs). However, polymer-based BVSs, such as Abbott's Absorb, the only US FDA-approved BVS, have had limited use due to increased strut thickness (157 μm for Absorb), exacerbated tissue inflammation, and increased risk of major cardiac events leading to inferior clinical performance when compared to metallic DESs. Herein we report the development of a drug-eluting BVS (DE-BVS) through the innovative use of a photopolymerizable, citrate-based biomaterial and a high-precision additive manufacturing process. BVS with a clinically relevant strut thickness of 62 μm can be produced in a high-throughput manner, i.e. one BVS per minute, and controlled release of the anti-restenosis drug everolimus can be achieved by engineering the structure of polymer coatings to fabricate drug-eluting BVS. We achieved the successful deployment of BVSs and DE-BVSs in swine coronary arteries using a custom-built balloon catheter and BVS delivery system and confirmed BVS safety and efficacy regarding maintenance of vessel patency for 28 days, observing an inflammation profile for BVS and DE-BVS that was comparable to the commercial XIENCE™ DES (Abbott Vascular).

The microfoundations of lean leadership: Monozukuri, Hitozukuri, Kotozukuri

This paper sets out to investigate the microfoundations of lean leadership. To do so, we explore the Japanese philosophical concepts of Monozukuri, Hitozukuri, and Kotozukuri, representing that passion and craftsmanship for making things, developing people, and making things happen. The paper delves into how to foster lean leadership as a pivotal success factor in adopting lean thinking and practices in a manufacturing firm. By addressing the research gap concerning leadership traits contributing to successful lean deployment, the study explores the microfoundations of lean leadership. The paper is based on a single, in-depth longitudinal case study that offers new insights into the microfoundations underpinning lean leadership at VELUX, a Danish rooftop window manufacturer. The research contributes valuable evidence to the existing lean leadership research discourse, where lean should be seen as a human learning system, illustrating how lean leaders can cultivate lean and how the Japanese philosophical concepts of Monozukuri, Hitozukuri, and Kotozukuri resonate in a Western context.

A CRITICAL ANALYSIS OF THE HVAC BUSINESS MODEL TO DETERMINE ITS SUCCESS CRITERIA

Air conditioning sales in Pakistan have scarcely risen in the recent decade. Air conditioning firms would face stiff competition in this stagnating industry. Some organizations realize that combining goods and services to meet consumer requirements and boost efficiency may boost profits. This Sovietization business model will shift distributors from product-centered to service-centered. Sovietization is not new in Pakistan's air conditioning sector, but it is not widely applied and some operations fail. Pakistan has limited ventilation and air conditioning business model studies. Successful Sovietization deployment requires understanding its components and their effects. This article examines the important success aspects of ventilation and air conditioning Sovietization in Pakistan and how they benefit stakeholders. The author will identify candidate success criteria through literature studies, expert interviews, and surveys. The Dematel approach ranks essential success elements. This research will improve knowledge of the Sovietization business model in Pakistan and foster innovation.

Machine learning evaluation in the Global Event Processor FPGA for the ATLAS trigger upgrade

The Global Event Processor (GEP) FPGA is an area-constrained, performance-critical element of the Large Hadron Collider's (LHC) ATLAS experiment. It needs to very quickly determine which small fraction of detected events should be retained for further processing, and which other events will be discarded. This system involves a large number of individual processing tasks, brought together within the overall Algorithm Processing Platform (APP), to make filtering decisions at an overall latency of no more than 8ms. Currently, such filtering tasks are hand-coded implementations of standard deterministic signal processing tasks.In this paper we present methods to automatically create machine learning based algorithms for use within the APP framework, and demonstrate several successful such deployments. We leverage existing machine learning to FPGA flows such as hls4ml and fwX to significantly reduce the complexity of algorithm design. These have resulted in implementations of various machine learning algorithms with latencies of 1.2 μs and less than 5% resource utilization on an Xilinx XCVU9P FPGA. Finally, we implement these algorithms into the GEP system and present their actual performance.Our work shows the potential of using machine learning in the GEP for high-energy physics applications. This can significantly improve the performance of the trigger system and enable the ATLAS experiment to collect more data and make more discoveries. The architecture and approach presented in this paper can also be applied to other applications that require real-time processing of large volumes of data.

E-Learning Experiences Among Nursing Students: A Scoping Review.

Electronic learning (e-learning) is a broader approach to learning that opens up new avenues for studying and teaching in many sectors of education outside of the standard classroom setting. This paper might enhance cultural competence among nursing students and the perspective of the lecturer. Literature databases of Cinahl "Ebsco", Pubmed, and Science Direct were searched, and 326 potentially relevant nursing research articles were reviewed between 2017 and 2022. Fourteen papers were found to meet the inclusion criteria. Fourteen articles were included for scoping review, themes include blended E-learning, nursing students' e-learning readiness, and The challenges with the E-learning system. First: Blended learning may be a beneficial educational strategy in nursing education but it also requires a cautious and carefully planned approach with sufficient time for each student. Second: E-readiness is an important component of academic success, and prospective e-learning users' capacity to use a new learning environment, as well as alternative technologies, is referred to as e-learning readiness. In addition, continuous readiness assessment is required to ensure the successful deployment of e-learning. Thirdly, Accessibility is the most significant challenge students experience; many other issues also challenge e-learning, such as Infrastructure, Ineffective Time Management, and a lack of Instant Communication. E-learning, on the other hand, can provide an alternate mode of education, blended learning Proven to be effective as a modern learning method, students' e-learning readiness was a significant predictor of their satisfaction and motivation in the classroom, there are still concerns about the instructional veracity of e-Learning, as well as how to analyze and overcome hurdles and fears in e-Learning. Specialized implications for practical ramifications include strategic management planning, curriculum enhancement, and raising standards at the lecturer level in several specific areas.

Analysis of Hydrogen Value Chain Events: Implications for Hydrogen Refueling Stations’ Safety

Renewable hydrogen is emerging as the key to a sustainable energy transition with multiple applications and uses. In the field of transport, in addition to fuel cell vehicles, it is necessary to develop an extensive network of hydrogen refueling stations (hereafter HRSs). The characteristics and properties of hydrogen make ensuring the safe operation of these facilities a crucial element for their successful deployment and implementation. This paper shows the outcomes of an analysis of hydrogen incidents and accidents considering their potential application to HRSs. For this purpose, the HIAD 2.0 was reviewed and a total of 224 events that could be repeated in any of the major industrial processes related to hydrogen refueling stations were analyzed. This analysis was carried out using a mixed methodology of quantitative and qualitative techniques, considering the following hydrogen value chain: production, storage, delivery and industrial use. The results provide general information segmented by event frequency, damage classes and failure typology. The analysis shows the main processes of the value chain allow the identification of key aspects for the safety management of refueling facilities.

Enhancing Primary Care and Mental Health Integration for Women Veterans with Complex Healthcare Needs Using Evidence-Based Quality Improvement.

Women Veterans with co-morbid medical and mental health conditions face persistent barriers accessing high-quality health care. Evidence-based quality improvement (EBQI) offers a systematic approach to implementing new care models that can address care gaps for women Veterans. This study examines factors associated with the successful deployment of EBQI within integrated health systems to improve primary care for women Veterans with complex mental health needs. Following a 12-site (8 EBQI, 4 control) cluster randomized study to evaluate EBQI effectiveness, we conducted an in-depth case study analysis of one women's health clinic that used EBQI to improve integrated primary care-mental health services for women Veterans. Our study sample included providers, program managers, and clinic staff at a women Veteran's health clinic that, at the time of the study, had one Primary Care and Mental Health Integration team and one women's health primary care provider serving 800 women. We analyzed interviews conducted 12 months, 24 months, and 4 years post-implementation and call summaries between the clinic and support team. We conducted qualitative thematic analysis of interview and call summary data to identify EBQI elements, clinic characteristics, and reported challenges and successes within project development and execution. The clinic harnessed core EBQI elements (multi-level stakeholder engagement, data-driven progress-monitoring, PDSA cycles, sharing results) to accomplish pre-defined project goals, strengthen inter-disciplinary partnerships, and bolster team confidence. Clinic characteristics that facilitated implementation success included prior QI experience and an organizational culture responsive to innovation, while lack of pre-existing guidelines and limited access to centralized databases posed implementation challenges. Successful practice transformation emerges through the interaction of evidence-based methods and site-specific characteristics. Examining how clinic characteristics support or impede EBQI adaptation can facilitate efforts to improve care within integrated health systems.

Forecasting the probability of successful deployment of internet of things and cloud computing in the building sector through consistent fuzzy preference relations method

Purpose This study aims to identify significant factors, analyse them using the consistent fuzzy preference relations (CFPR) method and forecast the probability of successful deployment of the internet of things (IoT) and cloud computing (CC) in Gujarat, India’s building sector. Design/methodology/approach From the previous studies, 25 significant factors were identified, and a questionnaire survey with personal interviews obtained 120 responses from building experts in Gujarat, India. The questionnaire survey data’s validity, reliability and descriptive statistics were also assessed. Building experts’ opinions are inputted into the CFPR method, and priority weights and ratings for probable outcomes are obtained to forecast success and failure. Findings The findings demonstrate that the most important factors are affordable system and ease of use and battery life and size of sensors, whereas less important ones include poor collaboration between IoT and cloud developer community and building sector and suitable location. The forecasting values demonstrate that the factor suitable location has a high probability of success; however, factors such as loss of jobs and data governance have a high probability of failure. Based on the forecasted values, the probability of success (0.6420) is almost twice that of failure (0.3580). It shows that deploying IoT and CC in the building sector of Gujarat, India, is very much feasible. Originality/value Previous studies analysed IoT and CC factors using different multi-criteria decision-making (MCDM) methods to merely prioritise ranking in the building sector, but forecasting success/failure makes this study unique. This research is generally applicable, and its findings may be utilised for decision-making and deployment of IoT and CC in the building sector anywhere globally.

Advancing the understanding of successful technology implementation factors within state DOTs: a maturity model perspective

In an effort to improve the security, dependability, and quality of infrastructure systems, the implementation of complex transportation construction and maintenance projects is essential. Building upon a previous study that identified six crucial factors for effectively implementing new and emerging technologies in the State Department of Transportations (DOTs), this research aims to comprehensively explore six critical factors using a maturity model perspective. DOTs have shown increasing interest in adopting emerging and wireless technologies, as evident in various Federal Highway Administration Every Day Counts initiative. This study employs a mixed-methods approach, using survey responses from DOT personnel to evaluate the relative significance of technology implementation factors and subfactors. The six factors for successful technology implementation explored in this study are Organization Structure, Information Technology Infrastructure, Data Security, Information Workflow, Personnel Training, and Stakeholder Engagement. The study also evaluates the relative importance of People, Process, and Technology for each of these six factors. The paper focuses on providing detailed insights into the different criteria of each technology implementation factor and subfactor to guide agencies in successful technology deployment. Understanding these factors is critical to deploying emerging technologies successfully, which, in turn, leads to much-needed efficiency and productivity in highway construction and asset management. The findings of this study can help DOTs prioritize their technology investments and ultimately contribute to the development of a more advanced and sustainable transportation infrastructure system.