Empirical Adjustment Research Articles

Fracture toughness using strain energy density for aluminum alloys with notched round bars

ABSTRACT This paper introduces a refined method for measuring the fracture toughness of aluminium alloys using notched round bar (NRB) specimens. By incorporating plasticity effects and empirical adjustments, a model based on strain energy density (SED) is proposed. The refined values were validated against experimental data for aluminium 6061-T6 and 2011-T6 alloys. The results demonstrate that the proposed method yields fracture toughness values closely matching the experimental results, underscoring its potential for standardisation and practical applications in material testing and engineering design.

Intraoperative neurophysiologic monitoring during cardiac surgery: an observational cohort study

ObjectiveTo evaluate the impact of intraoperative neuromonitoring (IONM) on stroke and operative mortality after coronary and/or valvular operations.MethodsThis was an observational study of coronary and/or valvular heart operations from 2010...

Comparing Point-of-Care Technology to ELISA Testing for Infliximab and Adalimumab Levels in Adult Inflammatory Bowel Disease Patients: A Prospective Pilot Study.

Introduction: Therapeutic drug monitoring (TDM) has proven to be a valuable strategy for optimizing biologic therapies, among which are anti-tumor necrosis factor (anti-TNF) treatments in inflammatory bowel disease (IBD). In particular, reactive TDM has been shown to manage treatment failures more cost-effectively than empirical dose adjustments for anti-TNF drugs. However, several challenges currently impede the widespread adoption of TDM in clinical practice, particularly addressing the delay between sample collection and result availability. To overcome this limitation, the use of point-of-care technology tests (POCTs) is a potential solution. Point-of-care technology tests are medical diagnostic tests performed at the site of patient care to provide immediate results, allowing for quicker decision-making and treatment. The current standard of care (SOC) for drug level measurement relies on the enzyme-linked immunosorbent assay (ELISA), a method that is time-consuming and requires specialized personnel. This study aims to evaluate a novel, user-friendly, and efficient POCT method (ProciseDx Inc.) and compare its performance with the SOC ELISA in assessing infliximab and adalimumab levels in blood samples from IBD patients. Methods: In this prospective, single-center study, we collected blood samples from IBD patients, both CD and UC, receiving infliximab (87 IBD patients; 50% UC and 50% CD) or adalimumab (60 patients; 14% UC and 48% CD) and we analyzed the blood's drugs levels using both the ProciseDx Analyzer POC and the SOC ELISA. We examined the correlation between the two methods using statistical analyses, including the Deming regression test. Additionally, we assessed the ease of use, turnaround time, and overall practicality of the POCT in a clinical setting. Results: The ProciseDx test demonstrated a strong correlation with the SOC ELISA for measuring both infliximab and adalimumab levels. In particular, the overall correlation between the ProciseDx POCT and the ELISA assessments showed an r coefficient of 0.83 with an R squared value of 0.691 (95% CI 0.717-0.902) for IFX measurements, and an r coefficient of 0.85 with an R squared value of 0.739 (95% CI 0.720-0.930). Conclusions: the ProciseDx POC test offers significantly faster turnaround times and is more straightforward to use, making it a viable alternative for routine clinical monitoring. Despite its promising potential, further refinement and validation of the ProciseDx test are necessary to ensure its effectiveness across diverse patient populations and clinical settings. Future research should focus on optimizing the POC tests' performance and evaluating its long-term impact on IBD management.

Comparison of Home Sleep Devices and Sleep Study Testing in Hypoglossal Nerve Stimulation Patients.

Hypoglossal nerve stimulation (HGNS) is an implantable therapy for obstructive sleep apnea (OSA). Therapy efficacy is currently confirmed by a formal sleep study after empiric adjustment by the patient at home based on their subjective experience with the device. Home-based longitudinal apnea hypopnea index (AHI) measurements have the potential to refine HGNS therapeutic amplitude selection with objective data. Our objective was to compare AHI derived from routine sleep studies and two different home sleep devices in new HGNS recipients. Prospectively enrolled patients receiving HGNS therapy were provided a Sleep Tracking Mat (Withings, Issy-les-Moulineaux, France) and NightOwl peripheral arterial tonometry (PAT) sensor (Ectosense, Leuven, Belgium) for longitudinal, home AHI monitoring from 1 to 6 months post-implant. Therapy efficacy was assessed at 3 and 6 months post-implant using in-lab polysomnography (PSG) or home sleep apnea test (HSAT). The sleep mat and PAT sensor AHI were compared against PSG and HSAT for accuracy of OSA severity identification. Sixty patients were enrolled across 5 centers and followed for 6 months. The sleep mat had sensitivity and specificity for identifying AHI <15 of 61% and 82% and AHI <30 of 77% and 100%. The PAT device had sensitivity and specificity for identifying AHI <15 of 57% and 77% and AHI <30 of 81% and 80%. The sleep mat and PAT sensor demonstrated high sensitivity and specificity for detection of mild and moderate OSA in patients with HGNS therapy and may enable longitudinal objective monitoring of HGNS efficacy in the home setting. 3 Laryngoscope, 2024.

Toxicity reference values for force health protection: Provisional occupational exposure guidelines

Force Health Protection programs in the U.S. Air Force endeavor to sustain the operational readiness of the warfighters. We have previously identified hundreds of chemical substances of interest and toxicity reference value (TRV) knowledge gaps that constrain risk based-decision-making for potential exposures. Multiple approaches to occupational TRV estimation were used to generate possible guideline values for 84 compounds (18% of the substances of interest). These candidate TRVs included values from international databases, chemical similarity (nearest neighbor) approaches, empirical adjustments to account for duration differences, quantitative activity relationships, and thresholds of toxicological concern. This present work describes derivation of provisional TRVs from these candidate values. Rodent bioassay-derived long-term worker Derived No-Effect Levels (DNELs) were deemed presumptively the most reliable, but only 19 such DNELs were available for the 84 substances with TRV gaps. In the absence of DNELs, the quality of the approaches and consistency among candidate values were key elements of the weight of evidence used to select the most suitable guideline values. The use of novel nearest-neighbor approaches, empirical adjustment of short term TRVs, and occupational exposure bands were found to be options that would allow occupational TRV estimation with reasonable confidence for nearly all substances evaluated.

Toxicity reference values (TRVs) for force health protection: Gap identification and TRV prediction

The mission of the Force Health Protection (FHP) program of the U.S. Air Force (USAF), sustaining the readiness of warfighters, relies on determinations of acceptable levels of exposure to a wide array of substances that USAF personnel may encounter. In many cases, exposure details are limited or authoritative toxicity reference values (TRVs) are unavailable. To address some of the TRV gaps, we are integrating several approaches to generate health protective exposure guidelines. Descriptions are provided for identification of chemicals of interest for USAF FHP (467 to date), synthesis of multiple TRVs to derive Operational Exposure Limits (OpELs), and strategies for identifying and developing candidate values for provisional OpELs when authoritative TRVs are lacking. Rodent bioassay-derived long-term Derived No Effect Levels (DNELs) for workers were available only for a minority of the substances with occupational TRV gaps (19 of 84). Additional occupational TRV estimation approaches were found to be straightforward to implement: Tier 1 Occupational Exposure Bands, cheminformatics approaches (multiple linear regression and novel nearest-neighbor approaches), and empirical adjustment of short term TRVs. Risk assessors working in similar contexts may benefit from application of the resources referenced and developed in this work.

A comparative study of the Zeigler-Nickols PID, and fuzzy-PID based speed controller BLDC motor

Because of their effectiveness, simplicity, and polyvalence, PID controllers are widely used in industrial applications. Within the field of electric motors, namely the brushless direct current motors (BLDC). To maximize the performance of BLDC motors, which are known for their dependability and efficiency, precise speed control is essential. PID must be adjusted each time they are used in an application in order to maximize performance. In this study, we presented a comparison between two PID controllers for Brushless DC motors (BLDC). The first suggested PID controller is based on the Ziegler-Nichols method, a well-known empirical adjustment and testing strategy, resulting in under optimal performance in the presence of optimum local solutions for large research spaces. The fuzzy inference system is used to adjust the controller parameters in the second PID controller. The performance with the proposed Zigler-PID and fuzzy- PID controllers is analyzed and compared using the developed MATLAB/simulink model. These results demonstrate the effectiveness of Fuzzy –PID in improving the precision of speed control and energy efficiency.

Calculation of production balances and costs in ladle furnace refining process with addition of scrap steel

As increasing the scrap steel ratio has become an important approach for energy conservation and emission reduction in the steel industry, the technology of multi-point scrap addition has emerged. Ladle furnace (LF) refining is a feasible step for scrap steel utilisation, which is often overlooked and relied solely on empirical adjustment, resulting in difficulty of precise control. It is necessary to conduct in-depth research on this process using scientific methods and explore the impact of scrap steel addition on other materials. A comprehensive calculation model was established by scientifically investigating material and thermal balances in the LF process in this article, and the impact of initial steel and scrap compositions, temperature, mass and price of scrap on production costs were analysed and compared based on the model. The results showed that as the carbon and silicon content in the initial steel increase by every 0.01% could on average reduce the production cost per ton of steel by 4.013 CNY and 0.8473 CNY, respectively, and increases by 0.123 CNY as the sulphur content increase every 0.001%. The impact of scrap composition on costs is similar to that of the initial molten steel. Increasing the carbon and sulphur content in scrap by every 0.01% will on average increase costs by 0.0765 CNY and 0.01 CNY, respectively. When the price difference between scrap and the crude steel price is within 450–500 CNY/t, increasing scrap addition will create more profit. The relative reduction in smelting time is 7.97 s as the scrap temperature increases every 50°C. The present study holds academic and industrial significance in improving LF efficiency and promoting the LF process towards a more sustainable direction.

A WebGIS-Based System for Supporting Saline-Alkali Soil Ecological Monitoring: A Case Study in Yellow River Delta, China

The monitoring and evaluation of soil ecological environment is very important to ensure the saline-alkali soil health and the safety of agricultural products. It is of foremost importance to, within a regional ecological risk-reduction strategy, develop a useful online system for soil ecological assessment and prediction to prevent people from suffering the threat of sudden disasters. However, the traditional manual or empirical parameter adjustment causes the mismatch of the hyperparameters of the model, which cannot meet the urgent needs of high-performance prediction of soil properties using multi-dimensional data in WebGIS system. For the end, this study aims to develop a saline-alkali soil ecological monitoring system for real-time monitoring of soil ecology in the Yellow River Delta, China. The system applied advanced web-based GIS, including front-end and back-end technology stack, cross-platform deployment of machine learning models, and a database embedded in multi-source environmental variables. The system adopts a five-layer architecture and integrates functions such as data statistical analysis, soil health assessment, soil salt prediction and data management. The system visually displays the statistical results of air quality, vegetation index and soil properties in the study area. It provides users with ecological risk assessment functions to analyze soil heavy metal pollution. Specially, the system introduces a tree structured Parzan estimator (TPE) optimized machine learning model to achieve accurate prediction of soil salinity. The TPE-RF model had the highest prediction accuracy (R2 = 94.48%) in the testing set in comparison with the TPE-GBDT model, which exhibited the strong ability for the nonlinear relationship between environmental variables and soil salinity. The system developed in this study can provide accurate saline-alkali soil information and health assessment results for government agencies and farmers, which is of great significance for agricultural production and saline-alkali soil ecological protection.

Effect of letermovir initiation on tacrolimus concentrations among lung transplant recipients receiving concomitant azole antifungal prophylaxis.

The antiviral letermovir has been increasingly used as off-label cytomegalovirus prophylaxis in solid organ transplant recipients. Observational studies have reported notable increases in tacrolimus (FK) exposure following letermovir; however, whether a significant interaction occurs in the setting of existing moderate-to-strong CYP3A4 inhibition is unknown. Therefore, the purpose of this study was to evaluate FK trough changes before and after letermovir among lung transplant recipients receiving azole antifungal prophylaxis. This retrospective cohort study included lung transplant recipients newly initiated on letermovir between 2019-2022 following valganciclovir intolerance. Tacrolimus doses and concentrations were collected up to 30 days before and after the letermovir start date. No pre-emptive FK dose adjustments occurred prior to letermovir initiation. Patients admitted to the hospital or lacking an appropriately timed trough in the pre- or post-period were excluded. A total of 78 lung transplant recipients receiving FK (1.5mg median total daily dose) and itraconazole (56.4%), isavuconazole (25.6%) or posaconazole (17.9%) prophylaxis were included. Letermovir was started at a median of 8.4 months post-transplant. The pre-/post-letermovir median FK trough was 9.6/9.0ng/mL (p = .151), median dose-corrected trough was 4.2/4.7ng/mL/mg (+11.9%, p = .032), and median weight-based dose-corrected trough was 362/326 [ng/mL]/[mg/kg/day] (-9.9%, p = .036). There was no significant difference in the proportion of patients within their goal trough range before and after letermovir initiation (62%vs. 72%, p = .229). Empiric FK dose adjustments do not appear warranted before letermovir initiation in lung transplant recipients receiving antifungal prophylaxis with moderate-to-strong CYP3A4 inhibitors.

Hygroscopic behavior of noni leaf powder (Morinda citrifolia L.) obtained by drying in foam layer

Studies on the ethnicbotanical aspect and properties of noni (Morinda citrifolia L.) reveal that noni leaves contain chemical compounds that are related to antibacterial, antioxidant, antiviral, antifungal, antitumor, anthelmintic, analgesic, inflammatory, hypotensive and immunostimulating, among these compounds are, for example, flavanoid glycosides. The drying process is, in turn, used as a way of preserving perishable products, such as plants, due to the removal of water available for microbial deterioration, keeping the properties of the material preserved. For proper storage and increasing the shelf life of the powder obtained after the drying process, it is important to know the adsorption isotherms. This study aims to survey the adsorption isotherms of powders obtained from noni leaves. The powders to be analyzed were obtained through the foam layer drying process in the maximum, minimum and central factorials of the complete experimental planning of previously carried out studies. For the mathematical adjustment of the obtained isotherms, three different mathematical models from the literature were tested, based on experimental moisture adsorption data, for non-linear regression analysis, using the Gauss-Newton and Levenberg-Marquardt methods. The isosteric heat of the powders was also calculated to understand the behavior of the adsorbed phase with temperature variation, in addition to the affinity between adsorbate and adsorbent. The noni leaf powder showed high hygroscopicity, ranging from 0.098 to 0.969 and the Oswin model was the one that best represented empirical adjustments describing the process. The isotherms were classified as Brunauer Type III. It was also possible to infer that the energy required to remove water present in the noni leaf powder decreases with increasing equilibrium humidity.

A hybrid model to overcome landslide inventory incompleteness issue for landslide susceptibility prediction

Landslide inventory incompleteness (LII) may significantly affect the model performance in landslide susceptibility mapping (LSM). However, traditional methods, including heuristic, statistical and deterministic models, cannot address LII issue. In this work, we introduce a novel hybrid LEO-MAHP model, blending landslide frequency, empirical adjustments, optimization functions, and multi-participated analytic hierarchy process to address it by taking Badong County as the study area. This hybrid model mitigates the drawbacks of data-heavy statistical approaches and subjective heuristic models by incorporating LII into weight determination. The findings show that the LEO-MAHP model demonstrates superior performance (AUROC = 0.809 and 0.805) over conventional statistical (AUROC = 0.714 and 0.770) and heuristic models (AUROC = 0.738 and 0.741) across different LII levels. We further discuss alternative LII solutions, proposing an updated landslide management strategy that accounts for climate change and human activities. Our findings underscore the necessity of evaluating LII before applying statistical or machine learning methods in LSM.

Identification of crash-prone segment on Cipularang toll road

Traffic safety is an important issue in the field of transportation. Toll roads are one of the infrastructures that are prone to crashes, because on toll roads drivers can drive their vehicles at high speeds so there is a high risk of having an crash, as happens every year on Cipularang Toll Road. This research will determine the crash-prone segments on Cipularang Toll Road section using the network screening method. Crash and traffic data for the last five years (2018-2022) were obtained from PT Jasa Marga Purbaleunyi. Identification of crash-prone segments carried out using ten performance indicators contained in HSM (2010), namely average crash frequency, crash rate, critical rate, Equivalent Property Damage Only average crash frequency, relative severity index, excess predicted average crash frequency using method of moment, Level of Service of Safety, excess predicted average crash frequency using SPF, probability of specific crash type exceeding threshold proportion, and expected average crash frequency with Empirical Bayes adjustment. The analysis results show that the five segments with the highest priority for treatment are KM 91-92, KM 92-93, and KM 86-87 (Jakarta-bound direction), KM 120-121 and KM 101-102 (Bandung-bound direction).

Immune outcomes of lung transplant recipients with different cytochrome P450 3A5 phenotypes after discontinuation of voriconazole antifungal prophylaxis.

Tacrolimus forms the backbone of immunosuppression regimens in lung transplant recipients (LTRs). It is extensively metabolized by cytochrome P450 (CYP) 3A5 enzymes, of which polymorphisms can significantly affect tacrolimus dose requirements. It is unknown how coadministration of tacrolimus with voriconazole, a potent CYP3A5 inhibitor, affects rejection rates or empiric dose adjustments needed after voriconazole discontinuation. This retrospective cohort study compares LTRs with poor (PR) versus intermediate/extensive (IE) CYP3A5 metabolizer phenotypes. The primary endpoint is cumulative immune outcomes within three months of voriconazole discontinuation; secondary endpoints include change in tacrolimus dose-to-concentration ratios after voriconazole discontinuation. Thirty-four patients underwent full analysis: 13 IE and 21 PR metabolizers. A higher proportion of IE metabolizers were African American (46.2%vs. 9.5%, p=.03). There was no significant difference in composite immune outcomes, though there was a proportionally higher frequency of new donor-specific antibody development in PR metabolizers (14.3%vs 7.7%, p=.56). Both groups required approximately 2.5 to 3-fold tacrolimus dose increases post-voriconazole discontinuation to re-attain therapeutic levels. This novel investigation sheds light on how CYP3A5 phenotype could be used to guide tacrolimus dosing, with the goal of preventing both toxicity and organ rejection.

Recent advances in computerized imaging and its vital roles in liver disease diagnosis, preoperative planning, and interventional liver surgery: A review.

The earliest and most accurate detection of the pathological manifestations of hepatic diseases ensures effective treatments and thus positive prognostic outcomes. In clinical settings, screening and determining the extent of a pathology are prominent factors in preparing remedial agents and administering appropriate therapeutic procedures. Moreover, in a patient undergoing liver resection, a realistic preoperative simulation of the subject-specific anatomy and physiology also plays a vital part in conducting initial assessments, making surgical decisions during the procedure, and anticipating postoperative results. Conventionally, various medical imaging modalities, e.g., computed tomography, magnetic resonance imaging, and positron emission tomography, have been employed to assist in these tasks. In fact, several standardized procedures, such as lesion detection and liver segmentation, are also incorporated into prominent commercial software packages. Thus far, most integrated software as a medical device typically involves tedious interactions from the physician, such as manual delineation and empirical adjustments, as per a given patient. With the rapid progress in digital health approaches, especially medical image analysis, a wide range of computer algorithms have been proposed to facilitate those procedures. They include pattern recognition of a liver, its periphery, and lesion, as well as pre- and postoperative simulations. Prior to clinical adoption, however, software must conform to regulatory requirements set by the governing agency, for instance, valid clinical association and analytical and clinical validation. Therefore, this paper provides a detailed account and discussion of the state-of-the-art methods for liver image analyses, visualization, and simulation in the literature. Emphasis is placed upon their concepts, algorithmic classifications, merits, limitations, clinical considerations, and future research trends.

Optimization of vacuum belt drying process conditions for Citri Reticulatae Pericarpium using genetic algorithm and particle swarm optimization

Vacuum Belt Drying (VBD) is commonly applied in the pharmaceutical industry and its optimization is often required for different product processing. This study focuses on the optimization of process parameters for vacuum belt drying of Citri Reticulatae Pericarpium using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). VBD equipment control parameters are numerous, and lack of mature commissioning methods. Optimizing VBD process parameters can improve production efficiency and reduce production costs. The objective function was based on a numerical model of VBD. This model accurately predicts the moisture content variation during the drying process of Citri Reticulatae Pericarpium with a mean squared error (MSE) value of 0.0353. After that, the model introduces the formula for the income-expenditure ratio as the industrial optimization objective. The multi-objective problem of high yield and low energy consumption was transformed into a single objective by minimizing the income-expenditure ratio. After process optimization, the income-expenditure ratio was reduced by 3.80 and 4.02% compared to the original process conditions. A comparison between GA and PSO optimization results revealed that PSO performed better in optimizing the VBD process conditions, demonstrating superior convergence speed and optimization outcomes. In conclusion, this research emphasizes the effectiveness of utilizing GA and PSO algorithms to optimize process parameters for VBD. The algorithms provide optimized process conditions for VBD, replacing traditional empirical adjustment methods. This study contributes to the advancement of VBD technology by providing a systematic approach for optimizing process parameters.

Cultural Adaptation and Validation of the Attitudes Towards Alcohol Scale for Health Professionals in Portugal

AbstractWe aimed to conduct an adaptation and validation of the Attitudes towards Alcohol, Alcoholism and Individuals with Alcohol Use Disorders Scale (EAFAA) which set to address the main groups of attitudes. It is composed in its original version by 50 items, divided into 4 factors: (1) work and interpersonal relationships with patients with alcohol consumption disorders; (2) the person with alcohol consumption disorders; (3) alcoholism (aetiology) and (4) alcoholic beverages and their use. The cultural adaptation from Brazilian to European Portuguese changes 22 items and the designation of one factor. The content validity index scale was 0.96. The psychometric properties were analysed through a cross-sectional study, with a convenience sample of 500 health professionals and students. The confirmatory factor analysis model with the same four (4) original factors, led to the elimination of 9 items. The scale revealed Cronbach’s alpha of 0.801. Empirical adjustment indices were satisfactory with Minimum Discrepancy Function by Degrees of Freedom divided (CMIN) (X2/gl) = 3.91; p &lt; 0001, RMSEA = 0.076 and the goodness of fit index (GFI) with 0.8. Relative indices values were normal fit index (NFI) = 0.6, comparative fit index (CFI) = 0.7 and Tucker-Lewis index (TLI) = 0.7, with best parsimony indices PCFI = 0.595, PNFI = 0.530 and PGFI = 0.702.

Future change of summer hypoxia in coastal California Current

The occurrences of summer hypoxia in coastal California Current can significantly affect the benthic and pelagic habitat and lead to complex ecosystem changes. Model-simulated hypoxia in this region is strongly spatially heterogeneous, and its future changes show uncertainties depending on the model used. Here, we used an ensemble of the new generation Earth system models to examine the present-day and future changes of summer hypoxia in this region. We applied model-specific thresholds combined with empirical bias adjustments of the dissolved oxygen variance to identify hypoxia. We found that, although simulated dissolved oxygen in the subsurface varies across the models both in mean state and variability, after necessary bias adjustments, the ensemble shows reasonable hypoxia frequency compared with a hindcast in terms of spatial distribution and average frequency in the coastal region. The models project increases in hypoxia frequency under warming, which is in agreement with deoxygenation projected consistently across the models for the coastal California Current. This work demonstrated a practical approach of using the multi-model ensemble for regional studies while presenting methodology limitations and gaps in observations and models to improve these limitations.

Determination of Optimal Spatial Sample Sizes for Fitting Negative Binomial-Based Crash Prediction Models with Consideration of Statistical Modeling Assumptions

Transportation authorities aim to boost road safety by identifying risky locations and applying suitable safety measures. The Highway Safety Manual (HSM) is a vital resource for US transportation professionals, aiding in the creation of Safety Performance Functions (SPFs), which are predictive models for crashes. These models rely on negative binomial distribution-based regression and misinterpreting them due to unmet statistical assumptions can lead to erroneous conclusions, including inaccurately assessing crash rates or missing high-risk sites. The Florida Department of Transportation (FDOT) has introduced context classifications to HSM SPFs, complicating the assumption of violation identification. This study, part of an FDOT-sponsored project, investigates the established statistical diagnostic tests to identify model violations and proposes a novel approach to determine the optimal spatial regions for empirical Bayes adjustment. This adjustment aligns HSM SPFs with regression assumptions. This study employs a case study involving Florida roads. Results indicate that a 20-mile radius offers an optimal spatial sample size for modeling crashes of all injury levels, ensuring accurate assumptions. For severe-injury crashes, which are less frequent and harder to predict, a 60-mile radius is suggested to fulfill statistical modeling assumptions. This methodology guides FDOT practitioners in assessing the conformity of HSM SPFs with intended assumptions and determining appropriate region sizes.

Reinforcement-Learning-Based Composite Optimal Control for Looper Hydraulic Servo Systems in Hot Strip Rolling

The control scheme of the looper angle plays an essential role in hot rolling, which is directly related to the tension maintenance of the strip. The conventional scheme uses a proportion–integration–differentiation (PID) controller to control the servo valve to drive the hydraulic actuator. For different steel types and production temperature changes, the PID control mainly relies on empirical parameter adjustment, which may bring inaccuracy or inefficiency. The objective of this article is to propose a reinforcement-learning-based looper hydraulic servo optimization control scheme to automatically tune the control gain to optimum. First, the modeling error caused by variable parameters and the influence of external disturbance are considered, and the corresponding control model of the looper system is given. Subsequently, a feedforward controller with a radial basis neural-network-based disturbance observer is used to deal with modeling errors and disturbances. A feedback controller with off-policy reinforcement learning is applied in the meantime. The proposed control scheme can realize uniformly ultimately bounded of the system with Lyapunov theory. Simulation results verify the effectiveness of the proposed method.