Throughput Reduction Research Articles

A CAV-Lead speed advice approach considering local spatiotemporal traffic state near bottlenecks

Bottlenecks of the freeway generated especially by traffic accidents or temporary work zones contribute to significant reductions in system throughput and hinder the efficient traffic operations. It is imperative to take proactive measures to improve traffic state. With the rapid advancements in intelligent transportation, connected and autonomous vehicles (CAVs) have attracted much attention by its speculated capabilities in improving traffic safety and well-organized operational coordination. Therefore, reasonably utilizing the advantages of CAVs is possible to reduce the impact induced by bottlenecks. In this research, we propose a novel algorithm called CAV-Lead to obtain the CAV’s regulated speed under mixed CAVs and human-driven vehicles (HVs) environment to improve the overall utilization of the freeway capacity near bottlenecks. Firstly, we illustrate the basic principle of the CAV-Lead algorithm that takes both microscopic and macroscopic traffic characteristics into account. Then, based on the local spatiotemporal traffic state, the CAV-Lead algorithm is proposed to determine each CAV’s speed under mixed flow. Furthermore, a real-time simulation control framework considering the random behavior of HVs is presented. Moreover, several simulation evaluations including comparisons with basic scenarios and similar research are conducted under various CAV market penetration rates (MPRs). The results demonstrate that the CAV-Lead could improve the traffic performance, especially for the high traffic demand with certain MPRs.

Interventions to Reduce Surgical Waste Burden: A Systematic Review.

Operating suites are significant drivers of waste, pollution, and costs. Surgeons can help fight the climate crisis by implementing innovative strategies aimed at mitigating the environmental impact of surgical procedures and decreasing operational costs, and moving toward a more sustainable healthcare system. This study aims to review the literature describing interventions that reduce surgical waste. PubMed, Cochrane, and Embase were searched. Studies reporting interventions to reduce operative waste, including emissions, energy, trash, and other, were included. Case reports, opinion-based reports, reviews, and meta-analyses were excluded. Study quality was rated using MINORS and Jadad scales. Data were extracted from each study to calculate waste on a per case basis. Narrative review of studies was performed rather than meta-analysis. The search yielded 675 unique hits, of which 13 (level of evidence: I-III) met inclusion criteria. Included studies were categorized by intervention type in relation to the operating and procedure room. Three studies evaluated provider education initiatives, three evaluated setup of instruments, two evaluated single-use items, four evaluated technique changes, and one evaluated surgical venue. Seven studies reported significant reductions in disposable surgical waste throughput, and seven reported significant reductions in cost. The results of this systemic review demonstrated the effectiveness of surgical waste reduction initiatives in reducing waste volume, cost, and carbon emissions. Within plastic surgery, minimal surgical packs resulted in reduced gross waste and cost while promoting patient satisfaction in hand surgery, supporting the continued development and implementation of such initiatives in a surgical context.

Overcoming Remote Workforce Cyber Threats: A Comprehensive Ransomware and Bot Net Defense Strategy Utilizing VPN Networks

This study investigates endpoint security strategies for remote workforces utilizing VPN networks, focusing on mitigating ransomware and botnet attacks. A mixed-methods approach was employed, analyzing the effectiveness of existing endpoint solutions and simulating network segmentation strategies. The study highlights the enhanced effectiveness of traditional endpoint security solutions when augmented with advanced technologies with specific applications including email filtering to block phishing attempts, MFA to verify user identities, EDR systems to detect and block unauthorized access tools, and encryption to secure data during cloud services. The introduction of network segmentation and zero-trust architectures further secured data centers by limiting lateral movements and requiring continuous re-authentication. Results demonstrate that while traditional endpoint security solutions remain essential, their effectiveness can be enhanced through a multi-layered approach incorporating advanced technologies with this research showing quick response times, high containment efficiency, and fast recovery speeds across all segments, with the Finance Department notably achieving a response time of 5 minutes and containment efficiency of 95%. Specifically, our cost-benefit analysis of network segmentation strategies shows that Strategy 1, despite a higher cost, offers superior improvements in throughput and latency reduction, providing more value per dollar spent. These results underscore the plan’s capability in rapidly detecting, containing, and recovering from attacks. User education significantly improved cybersecurity awareness and reduced susceptibility to attacks. This research provides practical recommendations for organizations to strengthen their endpoint security posture and protect their remote workforce through a combination of advanced technologies, proactive measures, and continuous user education.

Against the promethean: Energy throughput and the far-right politics of degrowth

While criticism of growth by a diverse but overall left-leaning degrowth spectrum has become increasingly prominent, less is known about degrowth stances by far-right actors. While the far right is regularly viewed as ‘productivist’ and tied to fossil fuels, we point to a more complex relationship, taking the German New Right eco-magazine Die Kehre as a case study. Drawing on Bakhtin's concept of chronotope (time–space configuration), we identify two chronotopes, the promethean and the idyllic, with their interaction giving rise to a far-right degrowth stance. The promethean (rejected) signifies environmental destruction and consumerist ways of living. The idyllic (affirmed) posits a reduction in energy throughput, small-scale production/exchange, limits and the building of ‘rooted’ communities. Our analysis provides original conceptualization and one of the first comprehensive accounts of the far-right politics of degrowth. Thus, we raise awareness of how particularistic and non-universal criticisms of growth and capitalism can partly overlap with better known degrowth positions.

Achieving Accountability and Data Integrity in Message Queuing Telemetry Transport Using Blockchain and Interplanetary File System

Ensuring accountability and integrity in MQTT communications is important for enabling several IoT applications. This paper presents a novel approach that combines blockchain technology and the interplanetary file system (IPFS) to achieve non-repudiation and data integrity in the MQTT protocol. Our solution operates in discrete temporal rounds, during which the broker constructs a Merkle hash tree (MHT) from the messages received. Then the broker publishes the root on the blockchain and the MHT itself on IPFS. This mechanism guarantees that both publishers and subscribers can verify the integrity of the message exchanged. Furthermore, the interactions with the blockchain made by the publishers and the broker ensure they cannot deny having sent the exchanged messages. We provide a detailed security analysis, showing that under standard assumptions, the proposed solution achieves both data integrity and accountability. Additionally, we provided an experimental campaign to study the scalability and the throughput of the system. Our results show that our solution scales well with the number of clients. Furthermore, from our results, it emerges that the throughput reduction depends on the integrity check operations. However, since the frequency of these checks can be freely chosen, we can set it so that the throughput reduction is negligible. Finally, we provided a detailed analysis of the costs of our solution showing that, overall, the execution costs are relatively low, especially given the critical security and accountability benefits it guarantees. Furthermore, our analysis shows that the higher the number of subscribers in the system, the lower the costs per client in our solution. Again, this confirms that our solution does not present any scalability issues.

Streamlining the purification of a clinical-grade oncolytic virus for therapeutic applications

Oncolytic viruses (OV) have emerged as a promising approach to mitigate the challenges of treating solid cancers. However, the lack of established manufacturing and downstream processing (DSP) platforms combined with the high treatment doses needed for clinical use (108–1011 TCID50/dose), hamper the widespread success of this therapeutic concept. Here, we present an efficient and scalable GMP-compliant process for the purification of a fusogenic oncolytic virus (rVSV-NDV). Non-GMO CCX.E10 cells grown in suspension in chemically defined medium were used for high titer rVSV-NDV batch production (3.2 × 108TCID50/mL) in stirred tank bioreactors. All DSP unit operations (DNA digestion, clarification, chromatography, TFF, and sterilizing filtration) were optimized to identify the best purification approach. Among several strategies evaluated, two filters enabled high throughput and turbidity reduction, while preventing any loss of infectious particles during the clarification step. For the intermediate purification, anion-exchange chromatography (AEX) was used in combination with the addition of polyprotic salts, which resulted in a maximum recovery yield of 86 % of infectious particles. The addition of citrate to the chromatography setup increased the separation resolution of rVSV-NDV particles from less negatively charged impurities. Global recovery yield after four operation units was 64 % with 99 % and 97 % of protein and DNA clearance, respectively. Together, with our previously optimized upstream process, we open up an avenue for large-scale manufacturing of oncolytic VSV-NDV for future clinical use.

제미나이 출범에 따른 글로벌 주요 항만의 영향 분석

Purpose : The purpose of this study is to analyze the representative differences between the services implemented by the launch of the Gemini alliance and the existing alliance services. Additionally, it forecasts the impact of the Gemini launch on Busan Port and other top 20 global ports. Research design, data and methodology : An empirical analysis is conducted using data from the Gemini alliance and the top 20 global ports. Specifically, the analysis derives the impact on ports by utilizing the representative differences of the Gemini service, such as the size of the ships and the frequency of regular service calls, including GDP. Results : All three independent variables (ship size, frequency of regular service calls, and GDP) were found to have a significant impact on port throughput. In particular, the size of the ships and the frequency of regular service calls have a relatively greater impact compared to GDP. Conclusions : The frequency of regular service calls by Gemini at Busan Port is expected to decrease significantly, raising concerns about a potential reduction in Busan Port's throughput. However, considering the increase in the size of feeder ships and Gemini's market share of 20%, it is deemed unlikely that Busan Port's throughput will immediately decrease significantly due to the Gemini launch. Therefore, the success of the differentiated services promoted by Gemini is crucial.

Degrowth, global asymmetries, and ecosocial justice: Decolonial perspectives from Latin America

Abstract Degrowth literature predominantly states that degrowth strategies are meant from and for the Global North. While economic mainstream discourse suggests that the Global South still has to grow in terms of achieving development, degrowth proponents expect a reduction of material and energy throughput in the Global North to make ecological and conceptual space for the Global South to find its own paths toward ecosocial transformation. Based on a Latin American post-development and post-extractivist perspective and drawing on dependency theory, this article suggests another approach: first, it argues that the growth imperative, which in the peripheral world translates into the imperative to develop, also causes harm in societies of the Global South. Throughout Latin America, in the last decades, economic growth has mainly been achieved through extractivism with negative impacts, which are now being pushed further by green growth strategies. Second, I explore some possibilities for a cross-fertilisation between degrowth and International Relations scholarship, calling into question the assumption that degrowth in high-income countries would automatically ‘make space’ for the Global South to engage in self-determined paths of ecosocial transformation, as long as the structures, institutions, and rules of global governance and trade which secure profoundly asymmetric, colonial relations are not challenged.

A discrete-event simulation model for assessing operating room efficiency of thoracic, gastrointestinal, and orthopedic surgeries.

In hospital management, pinpointing steps that most enhance operating room (OR) throughput is challenging. While prior literature has utilized discrete event simulation (DES) to study specific strategies such as scheduling and resource allocation, our study examines an earlier planning phase, assessing all workflow stages to determine the most impactful steps for subsequent strategy development. DES models real-world systems by simulating sequential events. We constructed a DES model for thoracic, gastrointestinal, and orthopedic surgeries summarized from a tertiary Chinese hospital. The model covers preoperative preparations, OR occupation, and OR preparation. Parameters were sourced from patient data and staff experience. Model outcome is OR throughput. Post-validation, scenario analyses were conducted for each department, including: (1) improving preoperative patient preparation time; (2) increasing PACU beds; (3) improving OR preparation time; (4) use of new equipment to reduce the operative time of a selected surgery type; three levels of improvement (slight, moderate, large) were investigated. The first three improvement scenarios resulted in a 1%-5% increase in OR throughput across the three departments. Large reductions in operative time of the selected surgery types led to approximately 12%, 33%, and 38% increases in gastrointestinal, thoracic, and orthopedic surgery throughput, respectively. Moderate reductions resulted in 6%-17% increases in throughput and slight reductions of 1%-7%. The model could reliably reflect OR workflows of the three departments. Among the options investigated, model simulations suggest that improving OR preparation time and operative time are the most effective.

Packets distribution over asymmetric paths using concurrent multipath transfer

AbstractConcurrent multipath transfer (CMT) is a transport layer protocol used for transferring data over multiple paths concurrently. In the case of asymmetric paths, the dissimilarity of delay, bandwidth, and loss rate among paths leads to challenges such as out‐of‐order packets, receiver buffer blocking, and throughput reduction. A framework that uses the end‐to‐end delay of paths to distribute the packets over asymmetric paths in ordering policy is proposed. The proposed framework detects network congestion in the assigned path and reduces the congestion window. Furthermore, the proposed framework predicts receiver buffer blocking and deactivates the highest delay path that causes this problem. The simulation results show that the proposed framework achieves highest throughput, lower entropy and lower average application end‐to‐end delay than the previous algorithms.

Evaluation of Spatiotemporal Characteristics of Lane-Changing at the Freeway Weaving Area from Trajectory Data

Intensive lane-changing (LC) events are one of the great causes that make freeway weaving areas become bottlenecks. This study proposes an approach using vehicle trajectory data to investigate the spatiotemporal distributions of the number of LC events, void occupancies, and throughput variations at the freeway weaving area. Firstly, all LC events are extracted from the cleaned dataset and classified into four types according to the LC vehicles’ origin–destination lanes and LC directions. Secondly, the time and space void occupancies are calculated using the kinematic theory. Thirdly, the throughput variations are identified with the oblique N-curve method. Finally, the spatial and temporal distributions of the LC events, void occupancies, and throughput variations are plotted to analyze their characteristics and relationships. The spatial distributions of different types of LC events indicate that most LC events occur at the surrounding area of the on-ramp entrance. Spatial distributions of time void occupancies show that the time void in the original lanes is quite small while that in the target lanes is much larger. Furthermore, the time void occupancies amplify downstream when considering vehicles traveling on the road. By comparing the temporal distributions of LC events, void occupancies, and throughput variations, there is a lag effect between the large value occurrences of space void occupancy and throughput reduction and that of the LC events, which can conclude a causal relationship between LC events and the occurrences of the space void occupancies and throughput reductions.

Effectiveness of Vehicular Communication Using NP-CSMA with Bernoulli-Based Gaussian Interpolation Function

The objective is to investigate the effectiveness of Gaussian arrival and its effect on vehicular communication compared to Bernoulli's arrival. MATLAB simulation covers three different levels of slot probability: low, medium, and high. The goal behind such simulation is to establish the importance of an adaptive function such as Gaussian interpolation resulting in smoother control of vehicular communication with better channel performance when compared to Bernoulli. This work shows that at low slot probability (Pslot), Gaussian arrival results in a much higher throughput (S) compared to Bernoulli, with a gradual reduction in throughput as Gaussian spread (γ) increases. The decrease in S as γ increases is due to the Gaussian interpolation, which performs control and results in higher channel stability. At mid probability, the simulation and analysis show a convergence between Gaussian results and Bernoulli, with differences in buffered frames (Btotal) as a function of γ. At a high Pslot value, Bernoulli produces higher S than Gaussian, with the closest Gaussian values at γ=2. However, the number of buffered frames using Bernoulli arrival is much higher than Gaussian. The exceedingly high Btotal can result in more collisions, which Gaussian arrival controls very well with a small sacrifice in throughput. The shape function for Bernoulli is shown to be different from Gaussian, except for specific values of γ, where there is a match. The obtained results show the adaptability and smoothness in which Gaussian arrival can optimize channel communication using Non-Persistent CSMA, which enables intelligent vehicular communication.

Guided optimization of ToxPi model weights using a Semi-Automated approach

The Toxicological Prioritization Index (ToxPi) is a visual analysis and decision support tool for dimension reduction and visualization of high throughput, multi-dimensional feature data. ToxPi was originally developed for assessing the relative toxicity of multiple chemicals or stressors by synthesizing complex toxicological data to provide a single comprehensive view of the potential health effects. It continues to be used for profiling chemicals and has since been applied to other types of “sample” entities, including geospatial (e.g. county-level Covid-19 risk and sites of historical PFAS exposure) and other profiling applications. For any set of features (data collected on a set of sample entities), ToxPi integrates the data into a set of weighted slices that provide a visual profile and a score metric for comparison. This scoring system is highly dependent on user-provided feature weights, yet users often lack knowledge of how to define these feature weights. Common methods for predicting feature weights are generally unusable due to inappropriate statistical assumptions and lack of global distributional expectation. However, users often have an inherent understanding of expected results for a small subset of samples. For example, in chemical toxicity, prior knowledge can often place subsets of chemicals into categories of low, moderate or high toxicity (reference chemicals). Ordinal regression can be used to predict weights based on these response levels that are applicable to the entire feature set, analogous to using positive and negative controls to contextualize an empirical distribution. We propose a semi-supervised method utilizing ordinal regression to predict a set of feature weights that produces the best fit for the known response (“reference”) data and subsequently fine-tunes the weights via a customized genetic algorithm. We conduct a simulation study to show when this method can improve the results of ordinal regression, allowing for accurate feature weight prediction and sample ranking in scenarios with minimal response data. To ground-truth the guided weight optimization, we test this method on published data to build a ToxPi model for comparison against expert-knowledge-driven weight assignments.

Long-term simulation of an industrial coke breeze grinding circuit

One important application of rod milling is in size reduction of coke breeze, which is used as solid combustible in iron ore sintering plants. The work applies modeling and simulation of an industrial circuit comprised of a flip-flow screen and a rod mill to predict the circuit response in long-term operation, following the strategy used in the plant of replacing the rod charge only when it reaches the end of its life. Initially, the model for rod wear has been calibrated and the mill fillings estimated. Then the mill model has been validated with data from several industrial surveys, showing good agreement between the two. A long-term (approximately three-month) pseudo-dynamic simulation was then carried out to mimic the strategy normally used in plant operation. Additional simulations were then carried out to compare different scenarios of operation, demonstrating the benefit of increasing the rod charge and diameter, in particular when associated with operation of the mill at lower speed in the beginning of the charge life and progressive increase thereafter, besides partially replacing worn rods in their midlife. For instance, simulations showed that an increase in initial mill filling from 15 to 20 %, progressive increase in fraction of critical speed from 0.40 to 0.65 in contrast to operation at constant speed and gradual reduction of throughput from 60.0 to 40.2 t/h resulted in 56 % increase in charge lifetime, 35 % reduction in variability in product quality and 12 % reduction in cost of operation per ton of coke breeze produced.

Performance Analysis of Successive Cancellation Decoder for Polar Codes with Two Stage Channel Estimator

Nowadays polar codes are becoming one of the most favorable capacity achieving Error Correction Codes (ECC) for their simple low encoding and decoding complexity, have received much attention in recent years; they probably achieve the theoretical capacity of discrete memoryless channels using the low complexity successive cancellation (SC) decoding algorithm. However, among the very few prior successive cancellation polar decoder designs, the required long code length makes the decoding latency high. As a new polar decoder, referred to as 2b-SC-Precomputation decoder, reduce the latency from (2n-1) to ((3n/4)-1) without performance loss, which can reduce the hardware complexity by two stage channel estimators. Furthermore, in our method, the decoding latency decreases rapidly with increasing throughput. Significant latency and throughput reductions are shown by simulation results and report synthesis results for ASIC.

In-flight radiometric calibration of the Metis Visible Light channel using stars and comparison with STEREO-A/COR2 data

Context.We present the results for the in-flight radiometric calibration performed for the Visible Light (VL) channel of the Metis coronagraph on board Solar Orbiter.Aims.The radiometric calibration is a fundamental step in building the official pipeline of the instrument, devoted to producing the calibrated data in physical units (L2 data).Methods.To obtain the radiometric calibration factor (ϵVL), we used stellar targets transiting the Metis field of view. We derivedϵVLby determining the signal of each calibration star by means of the aperture photometry and calculating its expected flux in the Metis band pass. The analyzed data set covers the time range from the beginning of the Cruise Phase of the mission (June 2020) until March 2021.Results.Considering the uncertainties, the estimated factorϵVLis in a good agreement with that obtained during the on-ground calibration campaign. This implies that up to March 2021 there was no measurable reduction in the VL channel throughput. Finally, we compared the total and polarized brightness visible light images of the solar corona acquired with Metis and STEREO-A/COR2 during the November 2020 superior conjunction of these instruments. A general good agreement was obtained between the images of these instruments for both the total and polarized brightness.

On the Grant-Free Random Access in Multicell Massive MIMO Systems: Spatiotemporal Modeling and Backoff Scheme Optimization

Grant-free random access (GFRA) becomes attractive in Internet-of-Things (IoT) due to its low signaling overhead and short access latency. In this paper, we invetigate GFRA in a multicel massive multiple-input-multiple-output (MIMO) system. As the IoT device usually has sporadic traffic, we set a packet buffer for each device to describe its temporal traffic, and also use the stochastic geometry to describe the randomness of devices’ spatial locations. With backoff mechanism, only devices with a non-empty buffer and a successful backoff are activated and allowed to request access. Unlike previous works that regard all devices selecting the same pilot (i.e., the colliding devices) as undetectable, we give a more accurate model that the base station (BS) can detect colliding devices when they locate far away from each other, and we set a unique collision area for each device to quantify the boundary that the collision can be ignored. A tight approximation for the number of packets successfully transmitted at the unit area and time slot, named packet throughput, is derived. Based on it, we obtain the optimal backoff parameter that maximizes the packet throughput under devices’ delay constraints. It is shown that when pilots are insufficient or device packet traffic is heavy, a long backoff time is needed. However, as the pilot grows or the packet traffic turns light, devices should gradually reduce the backoff time. In particular, if pilots are surplus, cheap detectors can be equipped on the BS without an obvious packet throughput reduction.

Hardware Design and Implementation of Multiagent MLP Regression for the Estimation of Gunshot Direction on IoBT Edge Gateway

The advancements in the internet of things, artificial intelligence, and state-of-the-art computing techniques are the main pillars of the next generation defense technology. The internet of battlefield things (IoBT) with edge intelligence offers new opportunities for defense professionals for smart and effective military operations. The IoBT network connects soldiers by placing smart sensors on armors, weapons, body, and surroundings. This paper presents a novel edge intelligence based estimation of gunshot direction from the sensor-enabled glove for smart IoBT wearables. A multi-agent multi-layer perceptron (MA-MLP) and other regression models are developed and tested on the experimental dataset collected during this study. The MA-MLP model consists of two distinct MLP networks and a fusion block to estimate the gunshot direction. This paper demonstrates the effect of multiple subjects, sensor positions, and gun material on mean absolute error (MAE) of MA-MLP prediction. The software simulation results show that our proposed MA-MLP model has outperformed traditional machine learning techniques like linear regression, SVM and MLP with an MAE of 4.09°. Two different hardware designs i.e., intellectual property (IP) cores of the pre-trained MA-MLP model are implemented and tested on a field-programmable gate array (FPGA) for a system on a chip (SoC) based edge gateway. The first IP core requires 280 nanoseconds with a power consumption of 354 milliwatts while the second IP core requires 380 nanoseconds with 178 milliwatts power consumption per inference. Prediction accuracy of 97.48% with a reduction of throughput to 92.2% is achieved for both IP cores. This work is one of the first attempts to implement the FPGA-based edge intelligence for the IoBT wearables. The short computation time, low power consumption, small footprint, significant throughput reduction, desired accuracy, and processor offloading are achieved by both flexible hardware models designed explicitly for the edge intelligence.

Congenital Toxoplasmosis Diagnosis: Current Approaches and New Insights.

The aim of this study is to describe and discuss current disadvantages in congenital toxoplasmosis (CT) diagnosis, and what can be improved or changed through new perspectives and technological advances. We used Pubmed, Cochrane, and EBSCO databases to research publications from 10years to date describing current diagnostic methods for CT. The keywords used for this Mini-Review were Toxoplasma gondii, congenital toxoplasmosis, diagnosis, and prospects using Boolean operators such as AND, OR, identifying scientific publications highlighting the importance of implementing new diagnostic methods. Current diagnosis methods have several disadvantages, i.e., time-consuming, low sensitivity or specificity, and non-cost effective, that bring up the necessity of improving or developing new approaches. Recombinant proteins can help improve specificity by generating tests that use circulating strains in a specific geographical region, SAG1 and BAG1, as they are expressed during a particular stage of the disease (acute or chronic, respectively), for its use in serological diagnoses, such as capture ELISA and immunochromatography. Point of Care (POC) tests are methods performed at the patient care site, which leads to rapid patient treatment; despite the advantages, several improvements and perspectives are necessary to be implemented globally. Although already established diagnosis methods for CT may be sufficient in some regions, there is still a persistent demand to develop tests with higher throughput, cost, and time reduction in developing countries, where prevalence is high. New approaches in CT diagnosis, such as recombinant proteins, capture ELISA, immunochromatography, and POC tests methods, can increase performance in terms of specificity and sensitivity simplifying diagnostic tests' requirements.

Quantum healthcare computing using precision based granular approach

Previously, doctors interpreted diseases and their outcomes according to their experience in diagnosis. However, with the rapid increase in technology and population, the task of examining the patient becomes cumbersome and sometimes human efforts produce inconsistent results. Several research is being done for healthcare in terms of improving visualization and accuracy by using machine learning models. The current research targets to explore quantum computing as a different way of processing information compared to classical computer systems such as the use of quantum bits (qubits) along with superposition and entanglement for extending the computation capabilities at an unprecedented level of thinking in the healthcare domain. Quantum computing systems provide exponential benefits in terms of high-speed processing, faster and easier diagnostic assistance, unimaginable reduction in processing throughput, and many more. An extensive comparative analysis of existing approaches has been made which benchmarks the need for quantum healthcare computing. The objective of this work is to interpret whether Quantum computers prove to be more trusted when it comes to patient diagnosis, and faster analysis leading to cost optimization. In order to accelerate patient diagnosis, different approaches have been presented. The authors have proposed a precision-based granular approach for patient diagnosis that incorporates diagnosing the disease with enhanced precision and granularity. It involves reporting symptoms by the patient, encountering by healthcare expert on multiple factors, precise examination, granular health status (understanding past and present medical history), followed by a precise intervention by understanding biomolecular simulations. The algorithm has been presented to describe the flow process for patient diagnosis modeling using quantum computing. It involves qubits initialization, pairing the values, assigning probabilistic values, cross-validation, and quantum circuit formation. Precision-based granular approach has been implemented for a scenario (consisting of medical parameters such as oxygen and heart rate level, with the functionality of diagnosing oxygen level and heart range which lies as either normal or not normal (high/low)). Precision-based granular approach deals specifically with the individual ‘biomolecular simulation by understanding variations in the individual body whereas the umbrella-based approach does not deal with specifically to individual mechanisms. Granular level of encounter is not possible in umbrella-based treatment. Python Jupyter notebook and IBM Composer tool is used for the implementation of results. Bloch sphere and computational state graph are obtained as an output for better visualization and understanding. Falcon r5.11H processor is used with the version of 1.0.24 of IBM Composer to simulate the experiment. The methodology using precision based granular approach provides timely encounter of disease along with umbrella diagnosis and precise treatment. The time is taken and frequency of qubits have been presented with promising results. The diagnosis process and optimizing cost efficiency can aid in an early detection of the disease.