Throughput Time Research Articles

Enhanced principal component gradient round-robin load balancing in cloud computing

Cloud computing is a recent computer technology that has virtualized infrastructure to offer secure and reliable services to users in a complex environment. The main advantage of the cloud is its scalability. Vast scalability is possible because of load balancing. Due to the advancement of the cloud, huge numbers of service provisioning requests are generated from users. Cloud needs a better and more efficient load-balancing mechanism to handle all the user’s requests. This paper proposes the principal component gradient round robin load balancing (PCGRLB) technique to balance the workload in a cloud server for handling a huge workload within a minimum time. The PCGRLB technique performs two processes: Virtual machine analysis and load balancing. The PCGRLB technique is evaluated on factors such as throughput, makespan, and response time concerning several tasks. The results show that the PCGRLB technique offers an efficient solution in achieving higher load balance, efficiency, throughput, and minimized makespan and response time than the conventional techniques.

Sustainable optimizing WMN performance through meta-heuristic TDMA link scheduling and routing

Wireless mesh networks (WMNs) have become a popular solution for expanding internet service and communication in both urban and rural areas. However, the performance of WMNs depends on generating optimized time-division multiple access (TDMA) schedules, which distribute time into a list of slots called superframes. This study proposes novel meta-heuristic algorithms to generate TDMA link schedules in WMNs using two different interference/constraint models: multi-transmit-receive (MTR) and full-duplex (FD). The objectives of this study are to optimize the TDMA frame for packet transmission, satisfy the constraints, and minimize the end-to-end delay. The significant contributions of this study are: (1) proposing effective and efficient heuristic solutions to solve the NP-complete problem of generating optimal TDMA link schedules in WMNs; (2) investigating the new FD interference model to improve the network capacity above the physical layer. To achieve these objectives and contributions, the study uses two popular meta-heuristics, the artificial bee colony (ABC) and/or genetic algorithm (GA), to solve the known NP-complete problems of joint scheduling, power control, and rate control. The results of this study show that the proposed algorithms can generate optimized TDMA link schedules for both MTR and FD models. The joint routing and scheduling approach further minimizes end-to-end delay while maintaining the schedule's minimum length and/or maximum capacity. The proposed solution outperforms the existing solutions in terms of the number of active links, end-to-end delay, and network capacity. The research aims to improve the efficiency and effectiveness of WMNs in most applications that require high throughput and fast response time.

Performance enhancement of cloud security with migration algorithm for choosing virtual machines in cloud computing

The management of the hardware as well as software resources entrusted to third-party service suppliers have shown to be extraordinarily remarkable and accessible with Cloud Computing (CC), a potential computing technology for the future. Yet, a major undertaking is characterized by security as well as the use of Virtual Machines (VMs) to perform a request. The main issue with using technology is security worries, which raises the need for a strong security system to safeguard data on the cloud. Thus, to address this problem, a method called Weighted Mean-based Radial Basis Function Network with Advanced Encryption Standard (WMRBFN-AES) is developed to improve cloud security along with the time of execution. Random keys are produced utilizing the Pseudo Random Number Generator (PRNG) technique, and the best keys are created employing the WMRBFN technique. In the WMRBFN technique, randomly generated keys are used as input, and the hidden layers yield an estimate associated with the secret key of the input. The output layer generates the most precise secret keys to encrypt the data. Encryption is carried out using the AES algorithm. The files about the encrypted data are kept in a cloud storage system. Reduced execution times for stakeholder demands (tasks) as well as optimal cloud resource utilization result from the selection of VMs, which significantly improves reliability. The Migration Algorithm (MA) is used in the cloud framework to optimize the selection of VMs to improve resource utilization and performance. The experimental findings of the suggested framework and the state-of-the-art frameworks are compared, and it was found that the new model performed better in terms of cost, resource utilization, service latency, throughput, and execution time.

Effect of the Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act Scribes Trial on Emergency Department Provider Productivity and Patient Throughput Times

BackgroundTo help improve access to care, section 507 of the VA MISSION (Maintaining Internal Systems and Strengthening Integrated Outside Networks) Act of 2018 mandated a 2-year trial of medical scribes in the Veterans Health Administration (VHA). ObjectiveThe impact of scribes on provider productivity and patient throughput time in VHA emergency departments (EDs) was evaluated. MethodsA clustered randomized trial was designed using intent-to-treat difference-in-differences analysis. The intervention period was from June 30, 2020 to July 1, 2022. The trial included six intervention and six comparison ED clinics. Two ED providers who volunteered to participate in the trial were assigned two scribes each. Scribes assisted providers with documentation and visit-related activities. The outcomes were provider productivity and patient throughput time per clinic-pay period. ResultsRandomization to intervention resulted in decreased provider productivity and increased patient throughput time. In adjusted regression models, randomization to scribes was associated with a decrease of 8.4 visits per full-time equivalent (95% confidence interval [CI] 12.4–4.3; p < 0.001) and 0.5 patients per day per provider (95% CI 0.8–0.3; p < 0.001). Intervention was associated with increases in length of stay of 29.1 min (95% CI 21.2–36.9 min; p < 0.001), 6.3 min in door to doctor (95% CI 2.9–9.6 min; p < 0.001), 19.5 min in door to disposition (95% CI 13.2–25.9 min; p < 0.001), and 13.7 min in doctor to disposition (95% CI 8.8–18.6 min; p < 0.001). ConclusionsScribes were associated with decreased provider productivity and increased patient throughput time in VHA EDs. Although scribes may have contributed to improvements in other dimensions of quality, further examination of the ways in which scribes were used is advisable before widespread adoption in VHA EDs.

Innovative Education Method for a More Effective, Faster, and Valued Training in Radiation Therapy Treatment Planning

Because of the automation of radiation therapy, competencies of radiation technologists (RTTs) change, and training methods are challenged. This study aims to develop, and pilot test an innovative training method based on lean management principles. A new training method was developed for lung cancer treatment planning (TP). The novelty is summarized by including a stable environment and an increased focus on the how and why of key decision making. Trainees have to motivate their decisions during TP process, and to argue their choices with peers. Six students and 6 RTTs completed this training for lung cancer TP. Effects of the training were measured by (1) quality of TP, using doses in organs at risk and target volumes, (2) perceived experiences (survey), measured at baseline (T0); after peer session (T1); and 6 months later (T2). Finally, training throughput time was measured. At T0, RTTs showed a larger intragroup interquartile range (IIR) (2.63Gy vs 1.51Gy), but lower mean doses to heart and esophagus than students (6.79Gy vs 8.49Gy; 20.87Gy vs 24.62Gy). At T1, quality of TPs was similar between RTTs and students (IIR: 1.39Gy vs 1.33Gy) and no significant differences in mean dose to heart and esophagus (4.48Gy vs 4.69Gy; 17.75Gy vs 18.47Gy). At T2, students still performed equal to RTTs (IIR: 1.07Gy vs 1.45Gy) and achieved lower maximum dose to esophagus (44.75Gy vs 46.45Gy). The training method and peer sessions were experienced positive: at baseline (T0): 8 score on a scale 1-10, directly after the peer sessions; (T1): 8 by the students and 7 by the RTTs, after 9 months; (T2): 9 by the students and 7 by the RTTs. Training throughput time decreased from 12 to 3 months. This training method based on lean management principles was successfully applied to training of RTTs for lung cancer TP. Training throughput time was reduced dramatically and TP quality sustained after 6 months. This method can potentially improve training efficiency in diverse situations with complex decision-making.

SLA-ORECS: an SLA-oriented framework for reallocating resources in edge-cloud systems

The emergence of the Fifth Generation (5G) era has ushered in a new era of diverse business scenarios, primarily characterized by data-intensive and latency-sensitive applications. Edge computing technology integrates the information services environment with cloud computing capabilities at the edge of the network. However, the evolving landscape of business models necessitates a unified edge architecture capable of accommodating diverse requirements, posing substantial challenges for service providers in meeting Service-Level Agreements (SLAs).In response to these challenges, we introduce SLA-ORECS. This innovative framework dynamically allocates dedicated and shared resources within the edge-cloud system to cater to service requests with varying SLAs, thereby facilitating performance isolation. Furthermore, we have developed an optimization algorithm to enhance the efficiency of SLA assurance during request dispatch.The evaluation of SLA-ORECS highlights its noteworthy performance improvements, particularly in terms of system throughput and average time consumption.

Bitcoin Layer Two Scaling Solutions: Lightening Payment Channels Network Comprehensive Review, Mechanisms, Challenges, Open Issues and Future Research Directions

Blockchain technology era is triggered due to current advancement in the decentralized paradigms via facilitating secure collaboration among untrusted entities consequently superseding the necessity for the trusted third parties’ existence. Notwithstanding its potential, blockchain scalability faces severe limitations such as low throughput, high fees, and confirmation time latency. Several scaling solutions have been proposed, including layer one solutions that substantially require fundamentally amending blockchain underlying infrastructure resulted in further scalability complications. Layer two solutions, specifically Lightning Network, is a successful cryptographic layer built atop of the Bitcoin blockchain that aims to alleviate these implications by deporting transaction processing outside blockchain while ensuring security and consensus inherited from blockchain without compromising its infrastructure. Payment channels form the core of the lightning network, facilitating fast and secure transactions between participants. However, the network encounters substantial obstacles associated with transaction amounts that exceed the current channel capacity, leading to payment failures and limiting its effectiveness. Payment Channel Networks (PCN) have been risen as an evolved replacement that solves payment channel’s issues, seeking to enhance transaction throughput and reduce confirmation delays by solving limited capacities shortages. This paper thoroughly examines PCN efficiency impacting factors that hinder its current adoption growth and avoiding its widespread employment. Additionally, existing solutions are reviewed and categorized based on the PCN building architecture. The research concludes by outlining potential research avenues and future directions to improve the efficiency and practicality of the Lightning Network PCN. This research contribution aids the advent of blockchain layer two technology and participates in its integration into real-world applications.

The Value of Generative AI for Qualitative Research: A Pilot Study

This mixed methods approach study investigates the potential of introducing Generative AI (ChatGPT 4 and Bard) as part of a deductive qualitative research design that requires coding, focusing on possible gains in cost-effectiveness, coding throughput time and inter-coder reliability (Cohen’s Kappa). This study involved semi-structured interviews with five domain experts and analyzed a dataset of 122 respondents that required categorization into six pre-defined categories. The results from using Generative AI coders were compared with those from a previous study where human coders carried out the same task. In this comparison, we evaluated the performance of AI-based coders against two groups of human coders, comprising three experts and three non-experts. Our findings support the replacement of human coders with Generative AI ones, specifically ChatGPT for deductive qualitative research methods of limited scope. The experimental group, consisting of three independent Generative AI coders, outperformed both control groups in coding effort, with a fourfold (4x) efficiency and throughput time (15x) advantage. The latter could be explained by leveraging parallel processing. Concerning expert vs. non-expert coders, minimal evidence suggests a preference for experts. Although experts code slightly faster (17%), their inter-coder reliability showed no substantial advantage. A hybrid approach, combing ChatGPT and domain experts shows the most promise. This approach reduces costs, shortens project timelines, and enhances inter-coder reliability, as indicated by higher Cohen's Kappa values. In conclusion, Generative AI, exemplified by ChatGPT, offers a viable alternative to human coders, in combination with human research involvement, delivering cost savings and faster research completion without sacrificing notably reliability. These insights, while limited in scope, show potential for further studies with lager datasets, more inductive qualitative research designs and other research domains.

Impact of a new post-emergency teleconsultation procedure on overcrowding.

Saint Vincent de Paul Hospital is using a post-emergency teleconsultation solution (TELESCOPE). Target patients are discharged early, freeing up examination rooms and hospital beds, and the patient's clinical evolution is monitored by teleconsultation 24 hours after discharge. This study aims to evaluate this pioneering procedure, firstly to assess the impact of TELESCOPE on ED overcrowding, and secondarily to evaluate 72-hour post-emergency hospitalization rates, throughput times, and patient satisfaction. This is a prospective, comparative, before/after type study. The National Emergency Department Overcrowding Scale (NEDOCS) score was used to measure the impact of TLC on ED congestion. It was calculated every hour, every day during the two study periods. The 72-hour post-emergency hospitalization rates were cross-compared for the patients who had a TLC, those who did not have a TLC during the same period, and those from the control period. The patient flow was evaluated by comparing the average length of stay of patients invited to a TLC against those from the control period. The patient's satisfaction was measured through a telephone survey. The mean difference in NEDOCS score is -3.1 [-4.14; -1.85]. Compared to the control phase, ED spent an additional 160 hours at a 'normal' level during the test period. The time spent in the 'busy' or 'overcrowded' categories was reduced by 129 and 19 hours. There is a significant reduction in the hospitalization rate between teleconsultation patients and all other patients. The patients discharged and invited to a TLC waited 87 minutes longer on average [56 minutes; 119 minutes]. Patient satisfaction is high (91.8%). Additional statistical strength would be needed to prove a reduction in overcrowding. The low hospitalization rate reflects adequate recruitment. ED visit times were unexpectedly longer. TELESCOPE seems useful for defining a new type of care. A further multicentre study is scheduled for 2024.

Microfluidic methods for the diagnosis of acute respiratory tract infections.

Acute respiratory tract infections (ARTIs) are caused by sporadic or pandemic outbreaks of viral or bacterial pathogens, and continue to be a considerable socioeconomic burden for both developing and industrialized countries alike. Diagnostic methods and technologies serving as the cornerstone for disease management, epidemiological tracking, and public health interventions are evolving continuously to keep up with the demand for higher sensitivity, specificity and analytical throughput. Microfluidics is becoming a key technology in these developments as it allows for integrating, miniaturizing and automating bioanalytical assays at an unprecedented scale, reducing sample and reagent consumption and improving diagnostic performance in terms of sensitivity, throughput and response time. In this article, we describe relevant ARTIs-pneumonia, influenza, severe acute respiratory syndrome, and coronavirus disease 2019-along with their pathogenesis. We provide a summary of established methods for disease diagnosis, involving nucleic acid amplification techniques, antigen detection, serological testing as well as microbial culture. This is followed by a short introduction to microfluidics and how flow is governed at low volume and reduced scale using centrifugation, pneumatic pumping, electrowetting, capillary action, and propagation in porous media through wicking, for each of these principles impacts the design, functioning and performance of diagnostic tools in a particular way. We briefly cover commercial instruments that employ microfluidics for use in both laboratory and point-of-care settings. The main part of the article is dedicated to emerging methods deriving from the use of miniaturized, microfluidic systems for ARTI diagnosis. Finally, we share our thoughts on future perspectives and the challenges associated with validation, approval, and adaptation of microfluidic-based systems.

Blockchain Database for Improved Recording of Property Deeds

Abstract The current manual process for property deed recording lacks automation, efficiency, and transparency. Real estate transactions require disjointed efforts from multiple parties, and they only become legally binding once they are recorded in a public, physical repository: the Registry of Deeds. Sensitive information is centralized, leaving it vulnerable to misuse, fraudulent activity, and destruction. A blockchain database offers real-time traceability for transactions and a peer-to-peer network to connect the tasks of attorneys, title agents, notaries, grantors, and grantees into a seamless process. Records in a blockchain database are immutable and protected from misuse. Replacing the manual recording process with an automated blockchain eliminates waste in time, money, and third-party efforts, providing grantors and grantees with added protection in the legitimacy of their transactions. This paper aims to provide a proof-of-concept prototype to replace the current paper-based traditional process with an electronic-based blockchain process. A full replacement of the existing relational database will improve throughput, error rates, and cycle times in the recording process. The solution comprises an immutable, decentralized blockchain database of property deeds with an interactive website to add and query records.

Evaluating the Impact of Cyberattacks on PLC Performance: A Systematic Implementation and Empirical Investigation

Programmable Logic Controller plays a pivotal role in the operation of Industrial Control Systems, which are widely used to monitor and control critical infrastructure and manufacturing processes. However, the rise of Industry 4.0, coupled with the increasing sophistication of cyber threats poses a significant risk to the security of PLC. This study implements a wide range of attacks on a PLC, including Code Injection, Man in the Middle, and a Denial-of-Service attack. The study further scrutinizes the impact of these attacks on the performance of the PLC with a view to developing effective security measures to mitigate cyber risks in Operational Technology systems. The study employs throughput and round trip time as the key metrics to evaluate PLC performance under normal conditions and during cyberattacks. The results indicate a significant degradation in the PLC performance metrics during the attacks, with DoS having the highest impact followed by Man in the Middle attack. These results highlight the need for robust cybersecurity measures to secure PLC from potential cyber threats.

Machine Learning-Driven Maintenance Order Generation in Assembly Lines

The automatic generation of maintenance orders facilitates the prompt detection and root cause analysis of deviations or failures in the assembly process. The aim of this project is to use supervised learning models to recognise deviations in the throughput times of a fully automated assembly process carried out by robots. The model identifies errors, categorises their causes and transmits the information back to the Enterprise Resource Planning (ERP) system. The data collected comes from a development and test assembly station with an industrial robot. The data set from the assembly station was expanded using agent-based simulation in order to train the four models Support Vector Machine, K-Neares Neighbour, Naive Bayes and Decision Tree. The SVM model proved to be the most suitable model for automatic fault detection with an accuracy of 99.51 %. The model was integrated into the assembly station and an algorithm was developed to automatically generate maintenance messages to transmit the failure code to the ERP system.

Throughput Time Predictions Along the Order Fulfilment Process

Throughput Time Predictions Along the Order Fulfilment Process

Simulation and Evaluation of Distributed consensus Network for Multi-Agent Systems for Sybil Attacks

Distributed average consensus represents the amount of computing of inputs held by multiple agents that communicate through peer-to-peer networks. Collaboration among operators is essential for any distributed standard consensus protocol as every specialist needs to contribute to different operators, typically the adjacent (neighbouring) operators. Internet-of-Things (IoT) implementation is challenging because of its heterogeneous, massively distributed nature. The challenges of this challenge can be addressed with blockchain-based platforms and technologies. Testing and evaluation platforms are required for Blockchain deployments in IoT. A realistic and configurable network environment is presented in this paper to evaluate consensus algorithms. Many blockchainevaluation platforms do not provide a configurable and realistic network environment or are tied to a specific consensus protocol. With our simulator, practitioners can evaluate how consensus algorithms affect network events in congested or contested scenarios to determine the best consensus algorithm. It is proposed to achieve this task by generalizing consensus methods. The Blockchain simulator employs Discrete event network simulations for increased fidelity and scalability. In addition to evaluating the time, state block rate (%), estimation error, average throughput, and simulation time, we evaluate the performance of the proposed techniques based on the number of peer nodes. A comparison of the average transaction delivery rate with a traditional protocol is shown. The proposed protocol has a higher throughput average than the traditional one.

Service Chain Placement by Using an African Vulture Optimization Algorithm Based VNF in Cloud-Edge Computing

The use of virtual network functions (VNFs) enables the implementation of service function chains (SFCs), which is an innovative approach for delivering network services. The deployment of service chains on the actual network infrastructure and the establishment of virtual connections between VNF instances are crucial factors that significantly impact the quality of network services provided. Current research on the allocation of vital VNFs and resource constraints on the edge network has overlooked the potential benefits of employing SFCs with instance reuse. This strategy offers significant improvements in resource utilization and reduced startup time. The proposed approach demonstrates superior performance compared to existing state-of-the-art methods in maintaining inbound service chain requests, even in complex network typologies observed in real-world scenarios. We propose a novel technique called African vulture optimization algorithm for virtual network functions (AVOAVNF), which optimizes the sequential arrangement of SFCs. Extensive simulations on edge networks evaluate the AVOAVNF methodology, considering metrics such as latency, energy consumption, throughput, resource cost, and execution time. The results indicate that the proposed method outperforms BGWO, DDRL, BIP, and MILP techniques, reducing energy consumption by 8.35%, 12.23%, 29.54%, and 52.29%, respectively.

Multi-Network Latency Prediction for IoT and WSNs

The domain of Multi-Network Latency Prediction for IoT and Wireless Sensor Networks (WSNs) confronts significant challenges. However, continuous research efforts and progress in areas such as machine learning, edge computing, security technologies, and hybrid modelling are actively influencing the closure of identified gaps. Effectively addressing the inherent complexities in this field will play a crucial role in unlocking the full potential of latency prediction systems within the dynamic and diverse landscape of the Internet of Things (IoT). Using linear interpolation and extrapolation algorithms, the study explores the use of multi-network real-time end-to-end latency data for precise prediction. This approach has significantly improved network performance through throughput and response time optimization. The findings indicate prediction accuracy, with the majority of experimental connection pairs achieving over 95% accuracy, and within a 70% to 95% accuracy range. This research provides tangible evidence that data packet and end-to-end latency time predictions for heterogeneous low-rate and low-power WSNs, facilitated by a localized database, can substantially enhance network performance, and minimize latency. Our proposed JosNet model simplifies and streamlines WSN prediction by employing linear interpolation and extrapolation techniques. The research findings also underscore the potential of this approach to revolutionize the management and control of data packets in WSNs, paving the way for more efficient and responsive wireless sensor networks.

(Invited) Nanoplasmonic Optofluidic Biosensors for Rapid and Precise Immune Functional Analysis in Personalized Immunotherapy

Cytokines, acting as intercellular mediators and modulators, are essential biomolecules that regulate diverse functions within the immune response. The rapid and accurate quantification of cytokine-based immune fingerprints is vital for effectively treating immune-related diseases, particularly in point-of-care settings where immediate treatment decisions depend on precise determinations of an individual patient's immune status. Addressing the pressing need for cytokine immunoassays that offer high sensitivity, throughput, multiplexing capability, and short turnaround times at low system complexity, this presentation will discuss novel plasmonic nanomaterial-based optofluidic cytokine biosensors for immune functional analysis ranging from whole blood to single-cell levels. The multiscale experimental and theoretical research will bridge the gap in fundamental understanding of the immune system, enhancing the applicability, diagnosis, and predictive power for immune diseases. The developed platforms aim to provide biologists and clinicians with the capability to real-time monitor patients' immune statuses, a transformative achievement with immense potential for safe, effective, and personalized immunotherapy.

Applied Research on Electronic Documentation and 3D Product Model Deployment in Production and Assembly Processes

The three-dimensional (3D) product model has become a tool that has transitioned from a legacy instrument, used in design, to an emerging technology applied to production and assembly processes. As this evolution has occurred, the need has developed to understand the value of deploying the 3D product model beyond the design phase. This research answers the question and solves the problem, does electronic documentation inclusive of the 3D product model add to the production workers’ ability to complete the production task? To answer this question, the methods used were that the research team tested how accurately and quickly a production and assembly team could build the product using interactive, electronic documentation, including the 3D product model, as a means to understand the design intent as opposed to printed bills of materials (BOMs) and two-dimensional (2D) paper drawings. The conclusions that can be drawn from this research are that the research found statistically significant improvements in the production throughput time (~10%), reductions in the direct labor hours per unit (~14%), and retained quality levels, when deploying electronic documentation, including the 3D product model, into the production and assembly processes. Through the deployment of the interactive 3D product model electronic documentation to the production floor, the organization also took a step towards creating a digital twin of the produced product and laid a foundation for the further adoption of Industry 4.0 practices. The novelty of the work and the areas where it goes beyond previous efforts in the literature concerns the current body of knowledge that does not demonstrate a repeatable methodology through which industry and other researchers can replicate the experiment on demonstrating economic value when deploying the 3D product model to production and assembly processes. In this paper, the authors aim to build on prior work to demonstrate a repeatable methodology for determining the economic value of 3D product model deployment in production and assembly processes through applied research.

Clinical Evaluation of Direct Reverse Transcription PCR for Detection of SARS-CoV-2 Compared to Conventional RT-PCR in Patients with Positive Rapid Antigen Test Results during Circulation of Emerging Viral Variants.

The emergence of the Omicron (B.1.1.529) variant of SARS-CoV-2 has precipitated a new global wave of the COVID-19 pandemic. The rapid identification of SARS-CoV-2 infection is imperative for the effective mitigation of transmission. Diagnostic modalities such as rapid antigen testing and real-time reverse transcription polymerase chain reaction (RT-PCR) offer expedient turnaround times of 10-15 min and straightforward implementation. This preliminary study assessed the correlation between outcomes of commercially available rapid antigen tests for home use and conventional reverse transcription polymerase chain reaction (RT-PCR) assays using a limited set of clinical specimens. Patients aged 5-99 years presenting to the emergency department for SARS-CoV-2 testing were eligible for enrollment (n = 5652). Direct PCR and conventional RT-PCR were utilized for the detection of SARS-CoV-2. The entire cohort of 5652 clinical specimens was assessed by both modalities to determine the clinical utility of the direct RT-PCR assay. Timely confirmation of SARS-CoV-2 infection may attenuate viral propagation and guide therapeutic interventions. Additionally, direct RT-PCR as a secondary confirmatory test for at-home rapid antigen test results demonstrated sensitivity comparable to conventional RT-PCR, indicating utility for implementation in laboratories globally, especially in resource-limited settings with constraints on reagents, equipment, and skilled personnel. In summary, direct RT-PCR enables the detection of SARS-CoV-2 with a sensitivity approaching that of conventional RT-PCR while offering expedient throughput and shorter turnaround times. Moreover, direct RT-PCR provides an open-source option for diagnostic laboratories worldwide, particularly in low- and middle-income countries.