Desired Level Of Precision Research Articles

An optimal goose lithium-ion batteries accurate and rapid RUL prediction method with automatic initial hyperparameters settings

Abstract Machine learning has emerged as a highly effective tool for addressing complex data problems, garnering significant attention in the field of equipment degradation and remaining service life prediction. Existing prediction models typically exhibit two primary shortcomings: on the one hand, the accuracy of life prediction reaches the desired level of precision while failing to achieve a sufficiently fast prediction speed, and on the other hand, generalization is not guaranteed while requiring the model to be robust. These two aspects present a significant challenge to the field of machine learning. In light of the aforementioned issues, we propose a prediction model based on the goose algorithm. Initially, we set the goose algorithm using adaptive initialization of the goose population to guarantee that the goose population is set at the appropriate interval, and we incorporate it into the extreme learning machine model through the improved goose algorithm. goose algorithm is used to predict the service life. Finally, we utilize different types of lithium batteries with varying operational conditions to conduct pertinent case studies to validate the proposed prediction model. The results demonstrated that the average accuracy was above 98% in all validated datasets. The shortest computation time was 0.19 s.

Uncertainty of the initial phase estimation of a sinewave in the presence of phase noise, additive noise or jitter

This paper presents the derivation of a simple analytical expression for the precision of the initial phase estimates obtained through a least-squares procedure. That expression depends on number of samples, phase noise standard deviation and additive noise standard deviation. This study is applicable to the estimates obtained through the sine fitting algorithm with known frequency and to the Discrete Fourier Transform algorithm. The analytical expression supplied was verified using a Monte Carlo simulation with numerically created data. The non-ideal phenomena treated in this work is the presence of jitter in the sampling instant, phase noise in the oscillator and additive noise in the system both of which must be normally distributed with a known value of standard deviation. It is useful information for an engineer in order to choose the proper number of samples to acquire to achieve a desired level of precision on the estimates made and thus carry out an efficient data acquisition with no unnecessary samples thus optimizing the test duration.

A Robotic Solution for Precision Smoothing and Roughening of Precast Concrete Surfaces: Design and Experimental Validation.

Prefabricated construction has pioneered a new model in the construction industry, where prefabricated component modules are produced in factories and assembled on-site by construction workers, resulting in a highly efficient and convenient production process. Within the construction industry value chain, the smoothing and roughening of precast concrete components are critical processes. Currently, these tasks are predominantly performed manually, often failing to achieve the desired level of precision. This paper designs and develops a robotic system for smoothing and roughening precast concrete surfaces, along with a multi-degree-of-freedom integrated intelligent end-effector for smoothing and roughening. Point-to-point path planning methods are employed to achieve comprehensive path planning for both smoothing and roughening, enhancing the diversity of textural patterns using B-spline curves. In the presence of embedded obstacles, a biologically inspired neural network method is introduced for precise smoothing operation planning, and the A* algorithm is incorporated to enable the robot's escape from dead zones. Experimental validation further confirms the feasibility of the entire system and the accuracy of the machining path planning methods. The experimental results demonstrate that the proposed system meets the precision requirements for smoothing and offers diversity in roughening, affirming its practicality in the precast concrete process and expanding the automation level and application scenarios of robots in the field of prefabricated construction.

Quantitative ultrasonic characterization of Ti-6Al-4V components with surface roughness

Ultrasonic immersion testing enables nondestructive characterization of material microstructure through quantitative linkages between wave propagation parameters and features of interest. However, accurate measurements of metrics such as wave speed and attenuation generally require smooth test samples to ensure the reflected wave packets are not distorted. Thus, rough surfaces are mechanically polished before ultrasonic testing, which compromises the nondestructive nature of the measurement, distorting the sample geometry and imparting local damage. This challenge is particularly salient with the advent of manufacturing processes, such as metal additive manufacturing, that result in rough samples that must be characterized for heterogeneities in their as-manufactured state. This work examines the influence of surface roughness on longitudinal wave speed and attenuation in wrought and additively manufactured Ti-6Al-4V specimens. Samples are measured in an ultrasonic immersion setup before and after mechanical polishing using varying transducer frequencies and focal profiles. The resulting data informs the error generated in ultrasonic measurements as a function of roughness parameters, thereby informing the sample preparation needed for desired precision levels. Lastly, new measurement protocols are explored to correct for the presence of surface irregularities in a point-by-point fashion. This work was supported by the Robert W. Young Award for Undergraduate Student Research.

Methods for the estimation of direct and indirect vaccination effects by combining data from individual- and cluster-randomized trials.

Both individually and cluster randomized study designs have been used for vaccine trials to assess the effects of vaccine on reducing the risk of disease or infection. The choice between individually and cluster randomized designs is often driven by the target estimand of interest (eg, direct versus total), statistical power, and, importantly, logistic feasibility. To combat emerging infectious disease threats, especially when the number of events from one single trial may not be adequate to obtain vaccine effect estimates with a desired level of precision, it may be necessary to combine information across multiple trials. In this article, we propose a model formulation to estimate the direct, indirect, total, and overall vaccine effects combining data from trials with two types of study designs: individual-randomization and cluster-randomization, based on a Cox proportional hazards model, where the hazard of infection depends on both vaccine status of the individual as well as the vaccine status of the other individuals in the same cluster. We illustrate the use of the proposed model and assess the potential efficiency gain from combining data from multiple trials, compared to using data from each individual trial alone, through two simulation studies, one of which is designed based on a cholera vaccine trial previously carried out in Matlab, Bangladesh.

Automation of Operations in Assembly of Battery Modules in Electric Vehicles

The assembly of battery modules in a battery electric vehicle (BEV) plant involves several critical operations, including tightening screws to make connections between various components inside the battery pack. This operation is generally performed manually using powered tools, but this approach poses several challenges, including electricity hazards, bottlenecks, and ergonomic risks. The manual tightening process in the battery module assembly could lead to inefficiencies and safety concerns. Additionally, manual tightening may not always achieve the desired level of precision, potentially affecting quality and reliability of the battery modules. Automation of tightening operations has the potential to address these challenges and improve the overall efficiency, safety, and quality of battery module assembly. By employing robots and other automation technologies, the assembly process can be streamlined, reducing bottlenecks, and minimizing the risk of error and electrocution. This paper provides proposals for potential applications where deployment of robotic automation in the battery module assembly context can be explored. The paper investigates key factors including equipment selection, and proposes conceptual scenarios based on these factors. The results provide a thorough understanding of battery module assembly challenges and suggest solutions to resolve the most pressing issues.

High frequency acoustic microscopy imaging of pellet cladding interface in nuclear fuel rods

In Pressurized Water Reactors (PWRs), fuel rods play a crucial role in generating nuclear energy. These rods consist of ceramic pellets, such as UO2 or (U,Pu)O2, enclosed in a zircaloy cladding tube, leaving an initial gap between the pellets and the cladding. As the reactor operates and the fuel undergoes irradiation, both the ceramic pellets and the zircaloy cladding experience transformations, causing the gap between them to gradually close. This phenomenon has a significant impact on the thermomechanical behavior of the fuel rod.Understanding the nature of the bonding that occurs during irradiation is essential for ensuring the safe and efficient operation of the reactor. To investigate the evolution of the contact state between the fuel pellets and the cladding during irradiation, a detailed analysis of the pellet-cladding interface after irradiation is necessary. However, traditional examination methods might be destructive or incapable of providing the desired level of precision and resolution.The Institute of Electronic and Systems at the University of Montpellier (IES – UMR CNRS 5214), in collaboration with the Alternative Energies and Atomic Energy Commission (CEA) and Electricité de France (EDF), has developed a specialized high-frequency acoustic microscope for imaging and non-destructively inspecting the pellet/cladding interface. The design of the acoustic microscope takes into account the complexity of the fuel rod's structure and the challenges associated with imaging the pellet/cladding interface by utilizing high-frequency ultrasound.In this paper, we present the ability of this acoustic microscope to acquire 2D images with controlled displacements of the sample rod along both its axial and circumferential directions thanks to a card with a high sampling frequency reaching 2 GHz. This capability is crucial because the geometrical, chemical, and mechanical properties of the fuel pellet-cladding contact are not uniform in these directions. By obtaining detailed acoustic images, we can identify specific regions where the fuel pellets and the cladding were in contact during irradiation. In this research, a resolution study is carried out to validate the microscope's ability to investigate the fuel rod and achieve the desired resolutions.Testing on real samples requires a specific configuration of the microscope, which must be adapted to the irradiation conditions. This is why, before proceeding to this stage, it is necessary to carry out tests on representative samples to validate the achievement of the desired resolution. So we’re also presenting the first acoustic images obtained on the zircaloy alloy claddings.

Historical, theoretical and methodological aspects related to static balance analysis: an integrative review of literature

The assessment of balance performance has been the subject of study by researchers from various fields for over a century. Although there is a significant number of scientific publications, Results can divergent due to the various methods and variables of impact. Objective: To review the specialized literature regarding the historical, theoretical, and methodological aspects related to the analysis of static balance. Method: The search was conducted in the PubMed, PsycINFO, Scielo, Web of Science, and Scopus databases using the SPIDER strategy. Results: 86 studies were selected, which revealed 40 variables that can influence balance performance. Conclusion: There is a need for more robust standards, both for quantitative and qualitative methods, with the complexity of the analysis being directly proportional to the desired level of precision in the results.

Sample size and genetic divergence: a principal component analysis for soybean traits

Biometric techniques, including the principal component analysis, are commonly applied in soybean genetic divergence studies. However, the sample size used in such studies is often determined empirically, neglecting its potential impact on inference interpretation. Therefore, this study aimed (i) to analyze the response of principal components to the number of plants sampled per experimental unit (plot); (ii) to determine the multivariate representative sample size, and (iii) to construct a methodology to predict sample size for principal components as a function of the precision level defined a priori. Six experiments were performed in two locations in Rio Grande do Sul, Brazil, with three experiments being carried out in each location. All experiments were conducted using a complete randomized block design with three repetitions, and 20 soybean genotypes were utilized, resulting in 360 plots (60 plots per experiment). From each plot, twenty plants were sampled, totaling 7200 plants. A resampling bootstrap procedure was applied to the principal component technique for 10 biometric traits. Posteriorly, the sample size was defined based on predefined precision levels, and power logistic models were parametrized to predict the sample size per experimental unit. The precision of the eigenvalues obtained from the principal component analysis gradually improves with larger sample sizes. Eigenvalues that capture a higher variance tend to require smaller sample sizes for accurate estimation. The fitted models demonstrated satisfactory predictive ability in determining the optimal number of plants per experimental unit, serving as a complementary tool for defining sample size at the desired precision level. Eighteen plants per experimental unit are enough to estimate the eigenvalues of the first two soybean principal components reliably. The developed predictive methodology and sample dimensioning per experimental unit can support future research aimed at identifying divergent genotypes, making them useful tools for soybean plant breeding programs.

On polynomial-time computation of high-dimensional posterior measures by Langevin-type algorithms

The problem of generating random samples of high-dimensional posterior distributions is considered. The main results consist of non-asymptotic computational guarantees for Langevin-type MCMC algorithms which scale polynomially in key quantities such as the dimension of the model, the desired precision level, and the number of available statistical measurements. As a direct consequence, it is shown that posterior mean vectors as well as optimisation based maximum a posteriori (MAP) estimates are computable in polynomial time, with high probability under the distribution of the data. These results are complemented by statistical guarantees for recovery of the ground truth parameter generating the data. Our results are derived in a general high-dimensional non-linear regression setting (with Gaussian process priors) where posterior measures are not necessarily log-concave, employing a set of local ‘geometric’ assumptions on the parameter space, and assuming that a good initialiser of the algorithm is available. The theory is applied to a representative non-linear example from PDEs involving a steady-state Schrödinger equation.

Interaction confinement and electronic screening in two-dimensional nanofluidic channels.

The transport of fluids at the nanoscale is fundamental to manifold biological and industrial processes, ranging from neurotransmission to ultrafiltration. Yet, it is only recently that well-controlled channels with cross sections as small as a few molecular diameters became an experimental reality. When aqueous electrolytes are confined within such channels, the Coulomb interactions between the dissolved ions are reinforced due to dielectric contrast at the channel walls: We dub this effect "interaction confinement." Yet, no systematic way of computing these confined interactions has been proposed beyond the limiting cases of perfectly metallic or perfectly insulating channel walls. Here, we introduce a new formalism, based on the so-called surface response functions, that expresses the effective Coulomb interactions within a two-dimensional channel in terms of the wall's electronic structure, described to any desired level of precision. We use it to demonstrate that in few-nanometer-wide channels, the ionic interactions can be tuned by the wall material's screening length. We illustrate this approach by implementing these interactions in Brownian dynamics simulations of a strongly confined electrolyte and show that the resulting ionic conduction can be adjusted between Ohm's law and a Wien effect behavior. Our results provide a quantitative approach to tuning nanoscale ion transport through the electronic properties of the channel wall material.

Pattern matching for high precision detection of LINE-1s in human genomes

BackgroundLong interspersed element 1 (LINE-1 or L1) retrotransposons are mobile elements that constitute 17–20% of the human genome. Strong correlations between abnormal L1 expression and several human diseases have been reported. This has motivated increasing interest in accurate quantification of the number of L1 copies present in any given biologic specimen. A main obstacle toward this aim is that L1s are relatively long DNA segments with regions of high variability, or largely present in the human genome as truncated fragments. These particularities render traditional alignment strategies, such as seed-and-extend inefficient, as the number of segments that are similar to L1s explodes exponentially. This study uses the pattern matching methodology for more accurate identification of L1s. We validate experimentally the superiority of pattern matching for L1 detection over alternative methods and discuss some of its potential applications.ResultsPattern matching detected full-length L1 copies with high precision, reasonable computational time, and no prior input information. It also detected truncated and significantly altered copies of L1 with relatively high precision. The method was effectively used to annotate L1s in a target genome and to calculate copy number variation with respect to a reference genome. Crucial to the success of implementation was the selection of a small set of k-mer probes from a set of sequences presenting a stable pattern of distribution in the genome. As in seed-and-extend methods, the pattern matching algorithm sowed these k-mer probes, but instead of using heuristic extensions around the seeds, the analysis was based on distribution patterns within the genome. The desired level of precision could be adjusted, with some loss of recall.ConclusionPattern matching is more efficient than seed-and-extend methods for the detection of L1 segments whose characterization depends on a finite set of sequences with common areas of low variability. We propose that pattern matching may help establish correlations between L1 copy number and disease states associated with L1 mobilization and evolution.

Bayesian spatial modeling of moose count data: increasing estimator efficiency and exploring ecological hypotheses

AbstractMoose management throughout much of Alaska and Canada relies on aerial count data, which are commonly collected using the geospatial population estimator (GSPE) protocol. The GSPE uses a model‐based analytical approach and finite‐population block kriging to estimate abundance from a collection of sampled survey units. Widespread implementation and well‐documented analytical software have resulted in reliable estimates of moose abundance, density, and composition across a large proportion of their range. Analysis is conducted almost exclusively using the GSPE software, which fits a fixed model structure to data collected within a single year. The downside of this approach to analysis is that the fixed model structure is inefficient for estimation, leading to more field effort than would otherwise be necessary to achieve a desired level of estimator precision. We developed a more easily modified and flexible Bayesian spatial general additive model approach (BSG) that accommodates spatial and temporal covariates (e.g., habitat characteristics, trend), multiple survey events, prior information, and incomplete detection. Using a series of 6 GSPE surveys conducted in Yukon‐Charley Rivers National Preserve, Alaska, USA, from 2003–2019, we established the equivalence of the 2 approaches under similar model structures. We then extended the BSG to demonstrate how a more comprehensive approach to analysis can affect estimator precision and be used to assess ecological relationships. The precision of annual abundance estimators from the BSG were improved by an average of 43% over those based on the standard GSPE analysis, highlighting the very real costs of assuming a fixed (i.e., suboptimal) model structure. The population increased at a rate of 2.3%/year (95% CrI = 0.8–3.8%), and the increase was largely explained by a parallel increase in wildfire extent (i.e., high quality moose habitat). These results suggest that our approach could be used to increase estimator efficiency or decrease future survey costs without any modifications to the basic protocol. While modification of the GSPE software is possible, practitioners may find the BSG approach more convenient for quickly developing model structures for a particular application, thereby allowing them to extract more information from existing and future datasets.

Highway Safety Manual Calibration: Estimating the Minimum Required Sample Size

Crash frequency has been identified by many experts as one of the most important safety measures, and the Highway Safety Manual (HSM) encompasses the most commonly accepted predictive models for predicting the crash frequency on specific road segments and intersections. The HSM recommends that the models be calibrated using data from a jurisdiction where the models will be applied. One of the most common start-up issues with the calibration process is how to estimate the required sample size to achieve a specific level of precision, which can be a function of the variance of the calibration factor. The published research has indicated great variance in sample size requirements, and some of the sample size requirements are so large that they may deter state departments of transportation (DOT) from conducting calibration studies. In this study, an equation is derived to estimate the sample size based on the coefficient of variation of the calibration factor and the coefficient of variation of the observed crashes. Using this equation, a framework is proposed for state and local agencies to estimate the required sample size for calibration based on their desired level of precision. Using two recent calibration studies, South Carolina and North Carolina, it is shown that the proposed framework leads to more accurate estimates of sample size compared with current HSM recommendations. Whereas the minimum sample size requirement published in the HSM is based on the summation of the observed crashes, this paper demonstrates that the summation of the observed crashes may result in calibration factors that are less likely to be equally precise and the coefficient of the variation of the observed crashes can be considered instead.

Evaluation of a fit-for-purpose assay to monitor antigen-specific functional CD4+ T-cell subpopulations in rheumatoid arthritis using flow cytometry-based peptide-MHC class-II tetramer staining.

Antigen-specific T cells can serve as a response biomarker in non-clinical or clinical immunotherapy studies in autoimmune disease. There are protocols with optimized multimer staining methods to detect peptide (p)MHCII+ CD4+ T cells, and some qualified and validated protocols for pMHCI+ CD8+ T cells. However, no protocol is fully or partially qualified to enumerate and characterize antigen-specific pMHCII+ CD4+ T cells from patient samples. Implementing such an assay requires a desired level of specificity and precision, in terms of assay repeatability and reproducibility. In transgenic type II collagen (CII)-immunized HLA-DR1/DR4 humanized mouse models of collagen-induced arthritis (CIA), CII259-273-specific T cells dominantly expand. Therefore antigen-specific T cells recognizing this epitope presented by rheumatoid arthritis (RA)-associated risk HLA-DR allomorphs are of interest to understand disease progression and responses to immunotherapy in RA patients. Using HLA-DRB1∗04:01 or ∗01:01-collagen type II (CII)259–273 tetramers, we evaluated parameters influencing precision and reproducibility of an optimized flow cytometry–based method for antigen-specific CD4+ T cells and eight specific subpopulations with and without tetramer positivity. We evaluated specificity, precision, and reproducibility for research environments and non-regulated laboratories. The assay has excellent overall precision with %CV<25% for intra-assay repeatability, inter-analyst precision, and inter-assay reproducibility. The precision of the assay correlated negatively with the cell viability after thawing, indicating that post-thaw viability is a critical parameter for reproducibility. This assay is suitable for longitudinal analysis of treatment response and disease activity outcome in RA patients, and adaptable for translational or immunotherapy clinical trial settings.

Popularity, novelty and relevance in point of interest recommendation: an experimental analysis

Recommender Systems (RSs) are often assessed in off-line settings by measuring the system precision in predicting the observed user’s ratings or choices. But, when a precise RS is on-line, the generated recommendations can be perceived as marginally useful because lacking novelty. The underlying problem is that it is hard to build an RS that can correctly generalise, from the analysis of user’s observed behaviour, and can identify the essential characteristics of novel and yet relevant recommendations. In this paper we address the above mentioned issue by considering four RSs that try to excel on different target criteria: precision, relevance and novelty. Two state of the art RSs called SKNN and s-SKNN follow a classical Nearest Neighbour approach, while the other two, Q-BASE and Q-POP PUSH are based on Inverse Reinforcement Learning. SKNN and s-SKNN optimise precision, Q-BASE tries to identify the characteristics of POIs that make them relevant, and Q-POP PUSH, a novel RS here introduced, is similar to Q-BASE but it also tries to recommend popular POIs. In an off-line experiment we discover that the recommendations produced by SKNN and s-SKNN optimise precision essentially by recommending quite popular POIs. Q-POP PUSH can be tuned to achieve a desired level of precision at the cost of losing part of the best capability of Q-BASE to generate novel and yet relevant recommendations. In the on-line study we discover that the recommendations of SKNN and Q-POP PUSH are liked more than those produced by Q-BASE. The rationale of that was found in the large percentage of novel recommendations produced by Q-BASE, which are difficult to appreciate. However, Q-BASE excels in recommending items that are both novel and liked by the users.

Suggested Sampling Methodology for Lake Benthic Macroinvertebrates under the Requirements of the European Water Framework Directive

The estimation of the number of samples required for reliably monitoring lakes’ benthic macroinvertebrates is difficult due to the natural variability and cost and time constraints. To determine a statistically robust and effective sampling design, we collected benthic macroinvertebrate samples from 15 Greek natural lakes. We compared the spatial and temporal variability of the benthic macroinvertebrate community composition to identify differences among lakes, between lake zones (sublittoral and profundal) and sampling periods. Furthermore, we examined the sampling precision and determined the number of required samples to attain maximum taxa richness. The diminution of the sampling effort was estimated and the desired precision level, considering different benthic macroinvertebrate abundances, was modelled. No temporal or spatial variation between lake zones was observed in communities’ compositions. The precision of our sampling design was adequate, and rarefaction curves revealed an adequate taxa richness (&gt;70%). The developed model could be applied to assess the required sampling effort in lakes within the Mediterranean ecoregion with similar benthic macroinvertebrate abundances.

Vision-Guided Hand–Eye Coordination for Robotic Grasping and Its Application in Tangram Puzzles

In this study we present an autonomous grasping system that uses a vision-guided hand–eye coordination policy with closed-loop vision-based control to ensure a sufficient task success rate while maintaining acceptable manipulation precision. When facing a diversity of tasks with complex environments, an autonomous robot should use the concept of task precision, including the accuracy of perception and precision of manipulation, as opposed to just the grasping success rate typically used in previous works. Task precision combines the advantages of grasping behaviors observed in humans and a grasping method applied in existing works. A visual servoing approach and a subtask decomposition strategy are proposed here to obtain the desired level of task precision. Our system performs satisfactorily on a tangram puzzle task. The experiments highlight the accuracy of perception, precision of manipulation, and robustness of the system. Moreover, the system is of great significance for improving the adaptability and flexibility of autonomous robots.

Sample size determination for a Bayesian cost-effectiveness model with structural zero costs

The idea that medical treatment costs and outcomes might be connected is not new. Likewise, as long as researchers have been designing clinical trials and public opinion polls, there has been interest in the sample size necessary to obtain a desired level of precision and certainty before collecting the data. However, researchers continue to adapt cost-effectiveness models for scenarios of ever-increasing complexity, and equally adaptable sample size determination schemes are required. Of interest for the current study are those instances wherein a non-trivial proportion of patients incur zero costs associated with their treatment. We propose a sample size determination scheme for a cost-effectiveness model fit to such a scenario. Furthermore, we display our method’s usefulness on multiple parameter configurations derived from applications presented in already published research.

Accuracy of Procalcitonin Levels for Diagnosis of Culture-Positive Sepsis in Critically Ill Trauma Patients: A Retrospective Analysis

BackgroundAbdominal trauma and intra-abdominal sepsis are associated with significant morbidity and mortality. Microcirculation in the gut is disrupted in hemorrhagic and septic shock leading to tissue hypoxia, and the damaged gut acts as a reservoir rich in inflammatory mediators and provides a continual source of inflammation to the systemic circulation leading to sepsis. Sepsis is defined as the presence (probable or documented) of infection together with a systemic inflammatory response to infection. Blood culture is commonly considered to be the preferred approach for diagnosing sepsis, although it is time-consuming, that is, reports are normally available only after 12-48 hours. Procalcitonin levels (PCT) have recently emerged as a promising biomarker in the diagnosis of sepsis. The aim of the present study is to determine the diagnostic accuracy of PCT levels in predicting sepsis in critically ill trauma patients.MethodologyThis was designed as a validation study conducted in the Indoor Department of General Surgery, Liaquat National Hospital, Karachi. The sample size was calculated by taking the estimated frequency of sepsis in suspected patients at 62.13%, expected sensitivity of PCT at 70.83%, and specificity at 84.21% and the desired precision level of 12% for sensitivity; the calculated sample size was 96. The non-probability consecutive sampling method was used to recruit participants who were diagnosed with sepsis on clinical assessment. Blood culture samples were sent for the enrolled patients and a final diagnosis was made on the blood culture report. PCT levels were measured in these suspected patients on the same day of sending blood culture. Diagnostic accuracy of PCT size was measured using the receiver operating characteristic (ROC) curve. ROC curve was formulated for PCT levels against culture-proven sepsis to determine the ideal cut-off value of PCT levels. Two different cut-offs were determined to obtain the highest sensitivity and highest specificity accordingly.ResultsA total of 97 individuals met the inclusion criteria with a mean age of 34.89 ± 10.52 years. Mean PCT levels were 0.96 ± 0.59, with a gender predilection towards females (p < 0.001). No age difference was documented among gender (p = 0.655). The mean duration of intensive care unit stay was 11.73 ± 3.56 days. Culture-proven sepsis was identified in 67.0% of the study participants with a higher PCT level (p < 0.001). Among the 52.6% males included in the study, half were reported to have culture-positive sepsis, but among the 47.4% females culture was positive in 87% (p < 0.001). ROC revealed PCT was predictive for culture-positive sepsis at a cut-off value 0.47 ng/mL (p < 0.001), with a sensitivity of 92.3%, specificity of 68.7%, positive predictive value (PPV) of 85.7%, and negative predictive value (NPV) of 81.5%. By increasing the cut-off value to 0.90 ng/mL at area under the curve of 0.816, the specificity increased to 81.3% and sensitivity became 66.2%, with a PPV of 87.8% and NPV of 54.2%.ConclusionOur study determined two cut-values for PCT to predict sepsis, one with the highest sensitivity and the other with better specificity. Other than that, higher PCT levels were significant in female trauma patients. We conclude that PCT is a reliable marker for culture-proven diagnosis of sepsis and may aid physicians/surgeons to promptly manage patients accordingly.