Correct Adjustment Research Articles

Real-time, sample-by-sample estimation of multiple signal waveforms from acoustic array data using B-splines

This work presents a real-time signal processing algorithm that estimates time-domain waveforms of multiple plane wave signals on a sample-by-sample basis from data measured by an acoustic array. Moreover, the algorithm provides sample-by-sample estimates of the direction-of-arrival (DOA) of the waveforms. In this case sample-by-sample means that as each sample of data is measured by the array estimates of the waveforms and their directions-of-arrival are updated. While the basic idea can be based on almost any finite-dimensional approximation to a function space, this algorithm makes use of B-spline basis for the signal waveforms. Other features of the approach include no restrictions on signal waveform shape, automatic estimation of the number of signals present, automatic step-size adjustment for correction of signal waveform and direction-of-arrival parameters to achieve good performance while guaranteeing stability, and the use of circular (spherical) statistical models for 2-D (3-D) DOA estimates. The approach contrasts with blind source separation (BSS) methods that are based on non-Gaussian statistical assumptions and that do not assume a known array geometry nor a propagation model. This method is closely related to algorithms based on block-of-data by block-of-data processing but does not require stitching results from block processing to create time-domain waveform estimates.

Influence of unidirectional composite failure envelope shape on predicting compressive failure of a laminate with a filled-hole

In recent decades, several computational models have emerged at the mesoscale for predicting the failure development and strength of composite materials. Nevertheless, the intricate failure mechanisms of composites pose a formidable challenge in developing models capable of consistently reproducing diverse sets of experimental data. Furthermore, the absence of standardised multiaxial tests has hindered consensus on failure envelopes and criteria for composites. In this work, an in-house constitutive model implemented in a finite element code is employed to predict the failure strength of several filled-hole compressive carbon/epoxy experimental tests, varying in stacking sequences, sizes, bolt preloads, and other parameters. The influence the selected failure criterion has on the predictions is analysed. The findings indicate a large impact of the failure envelope on both the failure compressive strength and developed failure mechanisms. Therefore, the correct adjustment of the failure criteria can be as relevant as defining accurately the material input parameters. These results emphasise the importance of accurately establishing failure envelopes, rather than overemphasising the development of complex models.

A Novel Approach for Position Verification and Dose Calculation through Local MVCT Reconstruction.

Traditional positioning verification using cone-beam computed tomography (CBCT) may incur errors due to potential misalignments between the isocenter of CBCT and the treatment beams in radiotherapy. This study introduces an innovative method for verifying patient positioning in radiotherapy. Initially, the transmission images from an electronic portal imaging device (EPID) are acquired from 10 distinct angles. Utilizing the ART-TV algorithm, a sparse reconstruction of local megavoltage computed tomography (MVCT) is performed. Subsequently, this MVCT is aligned with the planning CT via a three-dimensional mutual information registration technique, pinpointing any patient-positioning discrepancies and facilitating corrective adjustments to the treatment setup. Notably, this approach employs the same radiation source as used in treatment to obtain three-dimensional images, thereby circumventing errors stemming from misalignment between the isocenter of CBCT and the accelerator. The registration process requires only 10 EPID images, and the dose absorbed during this process is included in the total dose calculation. The results show that our method's reconstructed MVCT images fulfill the requirements for registration, and the registration algorithm accurately detects positioning errors, thus allowing for adjustments in the patient's treatment position and precise calculation of the absorbed dose.

The Oil:Water Ratio in the Vertical Centrifuge Separator and Its Influence in Phenolic Compounds in the Virgin Olive Oil and the Olive Mill Wastewater (Alpechín)

The use of the vertical centrifuge in the olive oil production process is generally assumed to be habitual and necessary for the elimination of both the vegetation water and the small olive pulp particles that are not eliminated during solid–liquid separation (horizontal centrifugation). Trials were carried out with different oil:water ratios to study the influence of this variable on both the quality parameters of the olive oils obtained and the loss of oil with the olive wastewater. The trials were carried out at the industrial mill level with oil:water ratios between 0.6 and 5.5. While no differences were observed in the quality parameters of the oils obtained, correct adjustment of the oil:water flow rates reduced the loss of phenols present in the oils by around 30%. In addition, the results show a direct relationship between the soluble effluent and the conductivity of the olive mill wastewater (alpechín) with the loss of oil in the effluent. This work proves that both oil quality and the competitiveness of the olive oil value chain can be increased with energy savings, water consumption reduction, and environmental sustainability.

Parametric analysis of thermal cycle of a short take-off and vertical landing engine

Abstract Short take-off/vertical landing (STOVL) engine is an emerging power source for fighter planes that can perform takeoff and landing operations in tight quarters. As the aforementioned function requires specific component design and matching, it is vitally necessary to study the impacts of various component states on engine performance and stability. This paper develops a performance model for STOVL engines based on nonlinear component models and validates the modelʼs correctness. Variable component working conditions and settings are altered, engine performance is forecasted and observed, and the mechanism underlying this trend is investigated. The results indicate that the performance of the STOVL engine is greatly influenced by the design parameters of the gas path, such as the bypass ratio and the injection volume of the roll nozzle, and is highly dependent on the correct adjustment of the rear bypass injector and the throat area of the nozzle.

Clinical and Genomic Predictors of Adverse Events in Newly Diagnosed Glioblastoma.

Adverse clinical events cause significant morbidity in patients with GBM (GBM). We examined whether genomic alterations were associated with AE (AE) in patients with GBM. We identified adults with histologically confirmed IDH-wild-type GBM with targeted next-generation sequencing (OncoPanel) at Dana Farber Cancer Institute from 2013 to 2019. Seizure at presentation, lymphopenia, thromboembolic events, pseudoprogression, and early progression (within 6 months of diagnosis) were identified as AE. The biologic function of genetic variants was categorized as loss-of-function (LoF), no change in function, or gain-of-function (GoF) using a somatic tumor mutation knowledge base (OncoKB) and consensus protein function predictions. Associations between functional genomic alterations and AE were examined using univariate logistic regressions and multivariable regressions adjusted for additional clinical predictors. Our study included 470 patients diagnosed with GBM who met the study criteria. We focused on 105 genes that had sequencing data available for ≥ 90% of the patients and were altered in ≥10% of the cohort. Following false-discovery rate (FDR) correction and multivariable adjustment, the TP53, RB1, IGF1R, and DIS3 LoF alterations were associated with lower odds of seizures, while EGFR, SMARCA4, GNA11, BRD4, and TCF3 GoF and SETD2 LoF alterations were associated with higher odds of seizures. For all other AE of interest, no significant associations were found with genomic alterations following FDR correction. Genomic biomarkers based on functional variant analysis of a routine clinical panel may help identify AE in GBM, particularly seizures. Identifying these risk factors could improve the management of patients through better supportive care and consideration of prophylactic therapies.

Using naturalistic and driving simulator data to model driver responses to unintentional lane departures

Unintentional lane departures on straight roads cause many road fatalities each year. The objective of this study was to explore and model drivers’ recovery steering maneuvers in unintentional drift situations, to enable the prospective safety benefit assessment of lane departure warning and avoidance systems through counterfactual simulations. The timing and amplitude of the steering adjustments drivers make to avoid lane departure were studied over three data sets with different origins, consisting of both naturalistic data and experimental data from a driving simulator study. With respect to timing, the main finding was that visually distracted drivers often initiate the corrective steering response prior to looking back towards the road, demonstrating that lane-keeping information in the visual periphery is sufficient to trigger the response. As for steering amplitude, the observed amplitudes were correlated against different lane departure risk metrics from the literature, resulting in a model capable of accounting for human behavior across all three data sets with good performance. Surprisingly, a very simple model (which describes the steering amplitude as increasing quadratically with the vehicle’s orientation to the road) predicted the amplitude of the primary corrective steering adjustment better than models based on more complex lane departure risk metrics. This result indicates that drivers scale the amplitude of their steering adjustment to the steering input needed to get the vehicle back in the lane already at first response. However, it was possible to obtain a similar model fit using a more complex threshold model, with different dynamics depending on the vehicle’s current orientation to the road. We discuss how these findings can be applied to models of human steering for safety benefit assessment.

Does Health Behavior Change After Diagnosis? Evidence From Fuzzy Regression Discontinuity

Abstract We investigate whether receiving health information changes human behavior by using a novel approach to inference in the fuzzy regression discontinuity design. The approach is robust to the strength of identification and allows for mean squared error optimal bandwidths as well as undersmoothing. It is based on the Anderson-Rubin test in the instrumental variable literature augmented with either robust bias correction or critical value adjustment. We find that the resulting confidence sets of the treatment effect are mostly wide or even unbounded. These findings indicate that we could not rule out most magnitudes of behavior change, including zero and non-zero ones.

A Cortical Mechanism Linking Saliency Detection and Motor Reactivity in Rhesus Monkeys.

Sudden and surprising sensory events trigger neural processes that swiftly adjust behavior. To study the phylogenesis and the mechanism of this phenomenon, we trained two male rhesus monkeys to keep a cursor inside a visual target by exerting force on an isometric joystick. We examined the effect of surprising auditory stimuli on exerted force, scalp electroencephalographic (EEG) activity, and local field potentials (LFPs) recorded from the dorsolateral prefrontal cortex. Auditory stimuli elicited (1) a biphasic modulation of isometric force, a transient decrease followed by a corrective tonic increase, and (2) EEG and LFP deflections dominated by two large negative-positive waves (N70 and P130). The EEG potential was symmetrical and maximal at the scalp vertex, highly reminiscent of the human "vertex potential." Electrocortical potentials and force were tightly coupled: the P130 amplitude predicted the magnitude of the corrective force increase, particularly in the LFPs recorded from deep rather than superficial cortical layers. These results disclose a phylogenetically preserved corticomotor mechanism supporting adaptive behavior in response to salient sensory events.Significance Statement Survival in the natural world depends on an animal's capacity to adapt ongoing behavior to abrupt unexpected events. To study the neural mechanisms underlying this capacity, we trained monkeys to apply constant force on a joystick while we recorded their brain activity from the scalp and the prefrontal cortex contralateral to the hand holding the joystick. Unexpected auditory stimuli elicited a biphasic force modulation: a transient reduction followed by a corrective adjustment. The same stimuli also elicited EEG and LFP responses, dominated by a biphasic wave that predicted the magnitude of the behavioral adjustment. These results disclose a phylogenetically preserved corticomotor mechanism supporting adaptive behavior in response to unexpected events.

Genetic causal association of adiposity, body fat distribution with incident heart failure in a European cohort: A linear and non-linear Mendelian randomisation analysis

Abstract Background Excess adiposity has been associated with higher risk of heart failure in observational studies. However, these observational studies are prone to residual confounding and the shape and strength of the independent associations of adiposity measures with heart failure is uncertain. Purpose This study aimed to compare the associations of usual and genetically determined general and central adiposity measures on incident heart failure in a European population cohort. Methods The MR analysis was performed in a cohort of 326,300 adults (mean age 55.9 years, 56% women) of European ancestry with complete genetic data and no prior cardiac and vascular disease recruited from UK general population in 2006-2010. Resurvey on a subset of 20,346 participants was conducted in 2012-13. Regression dilution ratios (RDR) of the different adiposity measures were calculated using Pearson’s correlation method to obtain usual levels of each adiposity measure. Participants were followed up by linkage to electronic health records for incident heart failure events. Externally weighted genetic risk scores (GRS) for body mass index (BMI) and waist circumference (WC) adjusted for BMI (WCadjBMI) were constructed using single nucleotide polymorphisms (SNPs) that were genome-wide significant for BMI and WC as reported by the Genetic Investigation of ANthropometric Traits (GIANT) consortium. Associations of usual measured adiposity measures and genetically predicted usual adiposity (76-SNP BMI-GRS [pleiotropic for other adiposity measures], 36-SNP BMI-GRS [non-pleiotropic for other adiposity measures] and 68-SNP WCadjBMI-GRS) with incident HF subtypes were determined using Cox proportional hazards regression models. The shapes of the associations of genetically predicted adiposity with heart failure at different levels of adiposity measures were examined using localized average causal effects within strata of residual adiposity measures after adjustment for GRS and correction for regression dilution. Hazard ratios (HRs) are presented as per equivalent 5 usual BMI units higher adiposity measure. Results Over a median follow-up of 12.8 years, there were 6,570 first-ever incident HF events. A positive non-linear association (J-shape) was observed between genetically predicted BMI and heart failure with while positive log-linear association was observed for genetically predicted WCadjBMI. Consistent with the observational analyses, the hazard ratio per 5 BMI units equivalent higher genetically predicted WCadjBMI were notably stronger (68-SNP WCadjBMI GRS: HR 2.03, 95% CI1.04-3.97) than for genetically predicted BMI (36-SNP GRS-BMI: HR 1.78, 95% CI 1.12-2.81 and 76-SNP GRS-BMI: HR 1.99, 95% CI 1.62-2.45). Conclusion The findings provide evidence for increased heart failure risk at extremes of BMI and also confirm the stronger causal role of central adiposity over general adiposity in heart failure across the whole range of waist circumference.Figure 1Figure 2

Identification of early decayed oranges using structured-illumination reflectance imaging coupled with fast demodulation and improved image processing algorithms

Effective detection of decayed oranges (Citrus genus) at the early stage is challenging because there are no or few visual symptoms on the infected fruit. Structured-illumination reflectance imaging (SIRI) has been proven effective for enhanced detection of subsurface defects in fruit. Amplitude component (AC) images retrieved from the original SIRI patterned images are useful for defect detection, but generally require acquiring three phase-shifted pattern images, which limits the imaging and detection speed. Moreover, the AC images may also suffer from noticeable uneven brightness due to fruit curvature, which affects the identification of decayed areas. This study was therefore aimed to explore faster image demodulation, enhancement and processing algorithms, based on a specially developed SIRI technology, for effective identification of early decayed oranges. Pattern images were acquired, using three phase-shifted sinusoidal illumination patterns at the wavelength of 810 nm and a spatial frequency of 0.20 cycles mm−1, from the orange samples infected with Penicillium digitatum fungus, the most serious and devastating pathogen for orange fruit. Two-dimensional spiral phase transform was used to obtain AC images from one or two pattern images. The acquired AC images were then processed by using simple brightness adjustment and integral image-based fast average filtering for brightness correction, and improved watershed algorithm and global threshold for segmentation of decayed areas. Three different combinations of these image processing procedures for single and two pattern images were proposed to distinguish decayed oranges from sound ones. The three methodologies all achieved high overall identification rates of 97.5%, 95.0% and 95.3%, when the stem-end effect was also considered. This study showed that accurate detection of early decayed orange fruit can be achieved by using one or two phase-shifted pattern images, which would be beneficial for real-time implementation of the technique.

Postprandial glucose metabolism in children and adolescents with type 1 diabetes mellitus: potential targets for improvement.

The main goal of therapeutic management of type 1 Diabetes Mellitus (T1DM) is to maintain optimal glycemic control to prevent acute and long-term diabetes complications and to enable a good quality of life. Postprandial glycemia makes a substantial contribution to overall glycemic control and variability in diabetes and, despite technological advancements in insulin treatments, optimal postprandial glycemia is difficult to achieve. Several factors influence postprandial blood glucose levels in children and adolescents with T1DM, including nutritional habits and adjustment of insulin doses according to meal composition. Additionally, hormone secretion, enteroendocrine axis dysfunction, altered gastrointestinal digestion and absorption, and physical activity play important roles. Meal-time routines, intake of appropriate ratios of macronutrients, and correct adjustment of the insulin dose for the meal composition have positive impacts on postprandial glycemic variability and long-term cardiometabolic health of the individual with T1DM. Further knowledge in the field is necessary for management of all these factors to be part of routine pediatric diabetes education and clinical practice. Thus, the aim of this report is to review the main factors that influence postprandial blood glucose levels and metabolism, focusing on macronutrients and other nutritional and lifestyle factors, to suggest potential targets for improving postprandial glycemia in the management of children and adolescents with T1DM.

Possibilities and limits of intraoperative 2D imaging in trauma surgery

The two-dimensional (2D) imaging represents an essential and cost-effective component of intraoperative position control in fracture stabilization, even in the era of new three-dimensional (3D) imaging capabilities. The aim of the present study, in addition to acurrent literature review, was to examine whether the intraoperative use of 2D images leads to aquality of fracture reduction comparable to postoperative computed tomographic (CT) analysis including 3D reconstructions. Acomparative retrospective analysis of intraoperative 2D and postoperative 3D image data was performed on 21 acetabular fractures stabilized via a pararectus approach according to an established protocol using the Matta criteria. The assessment of fracture reduction in intraoperative fluoroscopy compared with postoperative CT revealed adifference only in one case with respect to the categorization of the joint step reduction in the main loading zone. In the intraoperative use of 2D imaging for fracture treatment it is important to select the correct adjustment planes taking the anatomical conditions into account in order to achieve optimum assessability. In this way, the reduction result can be adequately displayed in fluoroscopy and is also comparable to the postoperative CT control. In addition, depending on the findings, optional intraoperative dynamic fluoroscopic assessment can have adirect influence on the further surgical procedure.

Winter wheat yield prediction using integrated Landsat 8 and Sentinel-2 vegetation index time-series data and machine learning algorithms

Timely and accurate forecasting of winter wheat yield is important to crop management, food security, and sustainable agricultural development. Unfortunately, the process of predicting winter wheat yield using satellite time series data often fails to capture complete and critical information about the crop growth process, which can restrict the accuracy of crop yield predictions. To overcome this challenge, it is necessary to increase the frequency of monitoring crop growth dynamics, identify suitable vegetation index (VI), and determine the optimal prediction model for time-series remote sensing data. In this study, we propose proposes a novel method for predicting winter wheat yield using integrated Landsat 8 (L8) and Sentinel-2 (S2) vegetation index time-series data and machine learning algorithms. Firstly, the integrated L8 and S2 dataset was obtained through the steps of cloud masking, re-sampling, re-projection, BRDF correction, and band adjustment. Then, the optimal VI was determined based on the association between the growth characteristics of winter wheat and the time-series characteristic curves of each VI. Subsequently, we employed Bayesian optimized CatBoost (BO-CatBoost) regression model to predict winter wheat yield, and compared this method with three other data-driven methods, including least absolute shrinkage and selection operator (LASSO), support vector regression (SVM), and random forest (RF). Our results showed that the winter wheat yield prediction accuracies reached the best performance using integrated Landsat 8 and Sentinel-2 WDRVI (Wide Dynamic Range Vegetation Index) time-series data and BO-CatBoost model. The R2 values were 0.70, 0.63, and 0.68, and RMSE values were 0.62, 0.73, and 0.62 t/ha for the years 2019 to 2021, respectively. In addition, acceptable accuracy was obtained for yield prediction in 2021 based on the model trained with historical data from 2019 and 2020. Moreover, this study demonstrated the result that winter wheat yield could be predicted about 40 days earlier using the proposed method. Finally, results showed that the harmonized data improved the yield estimation accuracies by a factor of 1.52, 1.29, and 1.13 compared with single S2 dataset for years 2019–2021. Various experiments demonstrated that the proposed method could effectively estimate and predict winter wheat yield data with good accuracy and robustness. This study provides technical support for improving the accuracy of winter wheat yield prediction and has the potential to be extended to yield estimation for other crops.

Missing Plants Effects and Stand Correction Methods in Coffea arabica Progeny Experiments

Plant loss occasionally occurs in field experiments with coffee crops in experimental plots. In breeding programs, such loss can be harmful, especially when the corresponding statistical analyses are not consistent with the experimentally generated data. Herein, we analyzed productivity data to determine whether the compensatory effect occurs in coffee crops, evaluated the need to correct experimental failures, and identified the best stand correction method. We used productivity data from six harvests of eleven experiments with Coffea arabica plants. The experiments were implemented in a randomized block design, with four replications and six plants per plot. The following stand correction methods were evaluated: rule of three; Zuber; Vencovsky and Cruz covariance of the average or ideal stands; and Cruz, and the data were compared without correction adjustments. The most adequate correction methods were selected based on genetic variance, selective accuracy, and progeny ordering. The compensatory effect was evident from the analyzed data, with stand correction being evidenced as beneficial in progeny competition experiments. The best results were obtained for the covariance methods using average or ideal stands, followed by the method proposed by Cruz. The rule of three and Zuber method exhibited unsatisfactory results and are not recommended for stand correction in progeny competition experiments with coffee crops.

The association of circulating endocannabinoids with neuroimaging and blood biomarkers of neuro-injury

BackgroundPreclinical studies highlight the importance of endogenous cannabinoids (endocannabinoids; eCBs) in neurodegeneration. Yet, prior observational studies focused on limited outcome measures and assessed only few eCB compounds while largely ignoring the complexity of the eCB system. We examined the associations of multiple circulating eCBs and eCB-like molecules with early markers of neurodegeneration and neuro-injury and tested for effect modification by sex.MethodsThis exploratory cross-sectional study included a random sample of 237 dementia-free older participants from the Framingham Heart Study Offspring cohort who attended examination cycle 9 (2011–2014), were 65 years or older, and cognitively healthy. Forty-four eCB compounds were quantified in serum, via liquid chromatography high-resolution mass spectrometry. Linear regression models were used to examine the associations of eCB levels with brain MRI measures (i.e., total cerebral brain volume, gray matter volume, hippocampal volume, and white matter hyperintensities volume) and blood biomarkers of Alzheimer’s disease and neuro-injury (i.e., total tau, neurofilament light, glial fibrillary acidic protein and Ubiquitin C-terminal hydrolase L1). All models were adjusted for potential confounders and effect modification by sex was examined.ResultsParticipants mean age was 73.3 ± 6.2 years, and 40% were men. After adjustment for potential confounders and correction for multiple comparisons, no statistically significant associations were observed between eCB levels and the study outcomes. However, we identified multiple sex-specific associations between eCB levels and the various study outcomes. For example, high linoleoyl ethanolamide (LEA) levels were related to decreased hippocampal volume among men and to increased hippocampal volume among women (β ± SE = − 0.12 ± 0.06, p = 0.034 and β ± SE = 0.08 ± 0.04, p = 0.026, respectively).ConclusionsCirculating eCBs may play a role in neuro-injury and may explain sex differences in susceptibility to accelerated brain aging. Particularly, our results highlight the possible involvement of eCBs from the N-acyl amino acids and fatty acid ethanolamide classes and suggest specific novel fatty acid compounds that may be implicated in brain aging. Furthermore, investigation of the eCBs contribution to neurodegenerative disease such as Alzheimer’s disease in humans is warranted, especially with prospective study designs and among diverse populations, including premenopausal women.

Interpretable data‐driven contingency classification for real‐time corrective security‐constrained economic dispatch

AbstractHigh penetrations of renewable energy are crucial for low‐carbon power systems. However, the higher volatility of renewable power generation pushes real‐time operations closer to equipment limits. It is thus important to utilize flexibilities in the system through corrective security‐constrained economic dispatch (SCED) that allows generators to take corrective adjustments after contingencies. The corrective SCED problem, containing a large number of contingencies, and corresponding post‐contingency decisions and constraints, is very large in scale and difficult to solve using purely model‐based methods within the strict time limits of real‐time markets. To accelerate the solution process, this paper develops a novel interpretable data‐driven contingency classification method. Historical data and their potentially useful patterns are utilized in interpretable data‐driven decision tree classifiers. To directly consider continuous features, such as net load values, and to consider imbalanced datasets without much additional complexity, Improved Strong Optimal Classification Trees (ISOCTs) are developed with new branching threshold constraints and category weights in the objective function. ISOCTs are then embedded into a hybrid model‐based and data‐driven framework to guarantee the accuracy of the real‐time active contingency set and the resulting security of dispatch decisions. Numerical testing results demonstrate the classification accuracy, computational efficiency, and interpretability of the proposed approach.

Bilateral tone mapping scheme for color correction and contrast adjustment in nearly invisible medical images

AbstractMedical images are commonly used to diagnose and screen various diseases. However, medical images taken by operators with different skill levels have a considerable variation in visual quality. Poor illumination of images increases uncertainty in clinical treatment and lead to the risk of misdiagnosis. This paper proposes a novel bilateral tone‐mapped gamma correction to enhance the visual quality of medical images without affecting the natural color quality. First, the proposed tone‐mapped gamma correction is employed to enhance the intensity values of poorly illuminated medical images. Then, bilateral enhancement curve adjustment is performed to effectively improve the global luminance content of the input image with features and richer information. The image quality assessment measures like entropy, blind/reference less image spatial quality evaluator, natural image quality evaluator, perception‐based image quality evaluator, contrast enhancement based image quality parameter, and color contrast fog density evaluates the superior performance of the proposed technique. The experimental results significantly enhance the intensity difference between dark and bright areas and can aid clinical experts and intelligent health fields.

Validity of leg length measurement in the supine and standing position compared with pelvic survey X-ray after total hip arthroplasty

IntroductionThe correct adjustment of leg length is a major goal in the implantation of total hip replacements (THRs). Differences in leg length can lead to functional impairment and patient dissatisfaction. By determining leg length at an early stage, before the patient is discharged from hospital, compensatory measures such as the production of special insoles or orthopaedic footwear can be initiated promptly if there is a difference in leg length. Due to shortening of the period of time spent in hospital, the traditional measurement of leg length in a standing position may be increasingly subject to error. A protective posture immediately after surgery or the presence of a twisted pelvis, for example, due to scoliotic spinal misalignments, falsifies the measurement result in the standing position. Here, the measurement of leg length in the supine position may prove to be accurate immediately postoperatively, regardless of potential sources of error, and is to be compared with measurement in the standing position versus radiological measurement on the AP pelvic survey.Material and methodsThe present retrospective study included 190 patients who had undergone primary total hip arthroplasty. The leg length difference (LLD) of the patients was determined pre- and postoperatively both in the supine and standing position and compared with the postoperative radiological pelvic survey image.ResultsPostoperatively, it was shown that the mean length measured was 0.35 mm too long in the supine position and 0.68 mm too short in the standing position (p value < 0.001). Determination of the average absolute measurement error produces a deviation of 4.06 mm in the standing and 4.51 mm in the supine position (p value 0.126).ConclusionsIt is shown that the postoperative measurement of LLD in the supine and standing position is equally valid and sufficiently accurate, compared with the gold standard of measurement on a radiograph.

Fixed-time attitude control for aircraft with strongly constrained actuators

This paper investigates a novel fixed-time attitude control method for trans-medium aircraft to ensure proper contact between the fuselage and the new medium before transmitting from different environmental mediums. How to achieve rapid corrective attitude adjustment from improper contact by utilizing the Reaction Control System with multiple discrete thrust gears that possess discrete thrusts with limited output has become a great challenge. To solve this problem, a novel fixed-time attitude controller is proposed. Firstly, a non-singular segmented terminal sliding mode surface is constructed to achieve a fixed convergence time and avoid the singularity problem. Subsequently, a command regulator is designed to cope with the derivation introduced by the strongly constrained actuators, which consists of the approximate thrust model and an auxiliary system based on a double power function. Furthermore, the stability analysis indicates the fixed time convergence stability of the closed-loop system and provides a clear calculation of the settling time. Finally, numerical comparison results and Monte Carlo simulations verify that the attitude can converge in a predetermined time from different initial states.