Mean Absolute Value Research Articles

Gaussian mutation–orca predation algorithm–deep residual shrinkage network (DRSN)–temporal convolutional network (TCN)–random forest model: an advanced machine learning model for predicting monthly rainfall and filtering irrelevant data

Monitoring water resources requires accurate predictions of rainfall data. Our study introduces a novel deep learning model named the deep residual shrinkage network (DRSN)—temporal convolutional network (TCN) to remove redundant features and extract temporal features from rainfall data. The TCN model extracts temporal features, and the DRSN enhances the quality of the extracted features. Then, the DRSN–TCN is coupled with a random forest (RF) model to model rainfall data. Since the RF model may be unable to classify and predict complex patterns and data, our study develops the RF model to model outputs with high accuracy. Since the DRSN–TCN model uses advanced operators to extract temporal features and remove irrelevant features, it can improve the performance of the RF model for predicting rainfall. We use a new optimizer named the Gaussian mutation (GM)–orca predation algorithm (OPA) to set the DRSN–TCN–RF (DTR) parameters and determine the best input scenario. This paper introduces a new machine learning model for rainfall prediction, improves the accuracy of the original TCN, and develops a new optimization method for input selection. The models used the lagged rainfall data to predict monthly data. GM–OPA improved the accuracy of the orca predation algorithm (OPA) for feature selection. The GM–OPA reduced the root mean square error (RMSE) values of OPA and particle swarm optimization (PSO) by 1.4%–3.4% and 6.14–9.54%, respectively. The GM–OPA can simplify the modeling process because it can determine the most important input parameters. Moreover, the GM–OPA can automatically determine the optimal input scenario. The DTR reduced the testing mean absolute error values of the TCN–RAF, DRSN–TCN, TCN, and RAF models by 5.3%, 21%, 40%, and 46%, respectively. Our study indicates that the proposed model is a reliable model for rainfall prediction.

Invariant Molecular Representations for Heterogeneous Catalysis.

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.

Three-dimensional surface area analyses of developing maxillary second premolar root apices for age estimation using CBCT images

Three-dimensional surface area analyses of developing root apices for age estimation in children and young adults have shown promising results. The current study aimed to apply this three-dimensional method to develop a regression model for estimating age in Malaysian children aged 7 to 14 using developing maxillary second premolars. A training sample of 155 cone-beam computed tomography scans (83 Malays and 72 Chinese) was analysed, and the formula was subsequently validated on an independent sample of 92 cone-beam computed tomography scans (45 Malays and 47 Chinese). The results showed a strong correlation (r = 94 %) between the chronological age as a dependent variable and the predictor variables, including root surface area of the apex, sex, ethnicity, and root development status (open/closed apices). For this model, the predictor variables accounted for 88.4 % of the variation in age except sex and ethnicity. A mean absolute error value of 0.42 indicated that this model can be reliably used for Malaysian children. In conclusion, this study recognises the method of three-dimensional surface area analyses as a valuable tool for age estimation in forensic and clinical practice. Further studies are highly recommended to assess its effectiveness across different demographic groups.

ACCURACY EVALUATION OF AUTOMATIC SEGMENTATION IN BIOMEDICAL IMAGES: 3D SPATIAL MAPPING OF SEGMENTATION ERRORS

Currently implemented accuracy metrics in open-source libraries for segmentation by supervised machine learning are typically one-dimensional scores [1]. While extremely relevant to evaluate applicability in clinics, anatomical location of segmentation errors is often neglected.This study aims to include the three-dimensional (3D) spatial information in the development of a novel framework for segmentation accuracy evaluation and comparison between different methods.Predicted and ground truth (manually segmented) segmentation masks are meshed into 3D surfaces. A template mesh of the same anatomical structure is then registered to all ground truth 3D surfaces. This ensures all surface points on the ground truth meshes to be in the same anatomically homologous order. Next, point-wise surface deviations between the registered ground truth mesh and the meshed segmentation prediction are calculated and allow for color plotting of point-wise descriptive statistics. Statistical parametric mapping includes point-wise false discovery rate (FDR) adjusted p-values (also referred to as q-values).The framework reads volumetric image data containing the segmentation masks of both ground truth and segmentation prediction. 3D color plots containing descriptive statistics (mean absolute value, maximal value,…) on point-wise segmentation errors are rendered. As an example, we compared segmentation results of nnUNet [2], UNet++ [3] and UNETR [4] by visualizing the mean absolute error (surface deviation from ground truth) as a color plot on the 3D model of bone and cartilage of the mean distal femur.A novel framework to evaluate segmentation accuracy is presented. Output includes anatomical information on the segmentation errors, as well as point-wise comparative statistics on different segmentation algorithms. Clearly, this allows for a better informed decision-making process when selecting the best algorithm for a specific clinical application.

Regression learner machine learning approach to predict wind speed considering various parameters and integration of DG in mesh distribution system through GWO

ABSTRACT The escalating global demand for electricity is driving a significant expansion in the size and complexity of electric power systems. Explosive growth strains power distribution, creating challenges for operators. Among these challenges, the imperative to minimise power losses is crucial. To enhance power efficiency and quality, Distributed Generation (DG) technology, particularly wind energy, is being integrated into distribution systems. However, the variable nature of wind speed poses a significant challenge to the seamless integration of wind energy into grids. To address this challenge, wind speed prediction methods are explored. In this work, a Machine Learning framework is employed for multivariate wind speed forecasting in Tamil Nadu, India 8.0883 ∘ N , 77.5385 ∘ E . Key performance metrics, such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error values (MAPE), are used to assess the accuracy of the wind speed predictions. The predicted wind speed data is then utilised to estimate wind farm power output which is seamlessly integrated into the distribution system. Load Flow analysis is conducted using the robust and straightforward current injection method, focusing on IEEE-33 and IEEE-69 bus-balanced Mesh distribution systems. The aim is to determine the impact of wind-powered Distributed Generation on power losses within these systems.

Sensitivity of the Causal Agent of Northern Leaf Blight of Corn, Exserohilum turcicum, to the Demethylase-Inhibiting Fungicide Flutriafol

Fungicides including active ingredients in the demethylation inhibitor group (Fungicide Resistance Action Committee Group 3) are used to manage corn diseases such as northern corn leaf blight, caused by Exserohilum turcicum. This study assessed the level of sensitivity to the demethylation-inhibiting fungicide flutriafol on a population of 81 E. turcicum isolates collected from across the United States in 2020. Sensitivity to flutriafol for E. turcicum was assessed using fungicide-amended media to determine the effective concentration needed to limit hyphal growth by 50%. The mean absolute EC50 values of the overall population calculated using the four-parameter log-logistic and Weibull2 four-parameter models were 2.1881 and 3.2643 µg/ml, respectively. The growth of most isolates ( n = 70, 86.4%) was completely inhibited by 1 µg/ml of flutriafol. However, the growth of seven isolates (8.6%) was only completely inhibited at 10 or 100 µg/ml, and complete inhibition was not observed at 100 µg/ml for four isolates (4.9%). These results suggest that there may be isolates of E. turcicum in the United States that are resistant to flutriafol. Additional fungicide sensitivity screening is critical to monitor the development of fungicide resistance in U.S. populations of E. turcicum.

Predicting band gaps of ABN3 perovskites: an account from machine learning and first-principle DFT studies.

The present paper is primarily focused on predicting the band gaps of nitride perovskites from machine learning (ML) models. The ML models have been framed from the feature descriptors and band gap values of 1563 inorganic nitride perovskites having formation energies <-0.026 eV and band gaps ranging from ∼1.0 to 3.1 eV. Four supervised ML models such as multi-layer perceptron (MLP), gradient boosted decision tree (GBDT), support vector regression (SVR) and random forest regression (RFR) have been considered to predict the band gaps of the said systems. The accuracy of each model has been tested from mean absolute error, root-mean-square error and determination coefficient R2 values. The bivariate plots between the predicted and input band gaps of the compounds for both the training and test datasets have also been estimated. Additionally, two ABN3-type nitride perovskites CeBN3 (B = Mo, W) have been selected and their electronic band structures and optoelectronic properties have been studied from density functional theory (DFT) calculations. The band gap values of the said compounds have been estimated from DFT calculations at PBE, HSE06, G0W0@PBE, G0W0@HSE06 level of theories. The present study will be helpful in exploring the ML models in predicting the band gaps of nitride perovskites which in turn may bear potential applications in photovoltaic cells and optical luminescent devices.

Oxazoline amino acid bioconjugates: one-pot synthesis and analysis of supramolecular interactions

Oxazoline amino acid bioconjugates were synthesized; the relative stability of a supramolecular dimer of a tris-derivative was investigated using mean absolute error values (MAE), derived from 1H NMR experiments and DFT calculations.

Does Exerting Grasps Involve a Finite Set of Muscle Patterns? A Study of Intra- and Intersubject Variability of Forearm sEMG Signals in Seven Grasp Types.

Surface Electromyography (sEMG) signals are widely used as input to control robotic devices, prosthetic limbs, exoskeletons, among other devices, and provide information about someone's intention to perform a particular movement. However, the redundant action of 32 muscles in the forearm and hand means that the neuromotor system can select different combinations of muscular activities to perform the same grasp, and these combinations could differ among subjects, and even among the trials done by the same subject. In this work, 22 healthy subjects performed seven representative grasp types (the most commonly used). sEMG signals were recorded from seven representative forearm spots identified in a previous work. Intra- and intersubject variability are presented by using four sEMG characteristics: muscle activity, zero crossing, enhanced wavelength and enhanced mean absolute value. The results confirmed the presence of both intra- and intersubject variability, which evidences the existence of distinct, yet limited, muscle patterns while executing the same grasp. This work underscores the importance of utilizing diverse combinations of sEMG features or characteristics of various natures, such as time-domain or frequency-domain, and it is the first work to observe the effect of considering different muscular patterns during grasps execution. This approach is applicable for fine-tuning the control settings of current sEMG devices.

Assessing the Efficacy and Safety of Intradermal Injection of Different Doses of Botulinum Toxin Type A: A Randomized, Double‐Blind, Placebo‐Controlled, Split‐Face Pilot Study in Rosacea Patients with Erythematic Telangiectasia

Introduction. Rosacea is a common chronic inflammatory skin disease of the central facial skin with unknown origin, significantly impacting quality of patient’s life and causing various psychosocial problems. Erythematotelangiectatic rosacea (ETR) is characterized by paroxysmal flushing that occurs repeatedly and is easily resistant to therapeutic drugs. While microinjection of type A botulinum toxin (BTX) can treat ETR, there is no consensus on the injection dose, and strong evidence to verify its efficacy and safety is lacking. This randomized, double‐blind, split‐face clinical study aimed to investigate the efficacy, safety, and optimal dose of two different single‐point injection doses (0.5 U and 1 U) of BTX in the treatment of rosacea. Methods. Twenty‐six patients with ETR were randomly assigned to receive different single‐point injections of BTX (0.5 U and 1 U, respectively) every 1 cm on one half of the face. The Clinical Erythema Score (CEA), VISIA red area absolute value, Global Aesthetic Improvement Scale Score (GAIS), and recurrence at 12‐week follow‐up were evaluated at baseline, 2,4, 8, and 12 weeks after injection. Additionally, the Dermatological Quality of Life Index (DLQI) questionnaire survey and adverse reactions were also recorded. Results. All twenty‐six patients completed the follow‐up visits and were included in the analysis. Compared to the 0.5 UBTX‐treated side, the CEA score showed significantly improvement in erythema and flushing at 2, 4, and 8 weeks after injection on the 1 U BTX‐treated side (P &lt; 0.05). The mean absolute value of the red area of VISIA was ‐19.12 ± 51.91 on 1 U BTX‐treated side and 2.5 ± 42.08, on 0.5 UBTX‐treated side at 4 weeks after treatment, showing significant improvement on the 1 U side (P &lt; 0.05). GAIS and DLQI were also significantly improved from Week 4 to Week 12 and Week 2 to Week 12, respectively (P &lt; 0.05). There was no recurrence of symptoms with either 0.5 U or 1 U injection by 12 weeks. Apart from one patient who experienced facial tightness and three patients who had temporary aggravation of erythema, all of which resolved without treatment, 22 patients did not report any side effects except for injection pain during the procedure. Conclusions. BTX‐A can significantly improves symptoms and quality of life in patients with refractory rosacea with few side effects. A single‐point injection of 1 U was more effective. This trial is registered with NCT06282679.

Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband

The deployment of electromyography (EMG) signals attracts many researchers since it can be used in decoding finger movements for exoskeleton robotics, prosthetics hand, and powered wheelchair. However, decoding any movement is a challenging task. The success of EMG signals' use lies in the appropriate choice of feature extraction and classification model, especially in the feature extraction process. Therefore, this study evaluates an eight-feature extraction evaluation on various machine learnings such as the Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Naïve Bayes (NB), and Quadratic Discriminant Analysis (QDA). The dataset from four intact subjects is used to classify twelve finger movements. Through 5 cross-validations, the result shows that almost all feature extractions combined with SVM outperform other combinations of features and classifiers. Mean Absolute Value (MAV) as a feature and SVM as a classifier highlight the best combination with an accuracy of 94.01%.

Machine learning to estimate the bond strength of the corroded steel bar‐concrete

AbstractSteel bar corrosion is prone to serious degeneration of the bond strength (τBS) of reinforced concrete, which leads to structural failure. There are many factors affecting the τBS; thus, the assessment of the τBS of corroded steel bar‐concrete becomes very complicated. In this research, a τBS evolution database of 430 samples containing 14 important factors, such as concrete compressive strength, yield strength and corrosion rate of steel bar, was established. Six machine learning algorithms were employed for comparative study to achieve optimal prediction results. The results showed that random forest and extreme random trees models have the best performance with coefficient of determination (R2) of 0.83 and 0.82, mean square error (MSE) of 13.59 and 13.98, root mean square error of 3.68 and 3.74, mean absolute error values of 2.35 and 2.31, and a20 values of 0.62 and 0.70, respectively. In addition, both models demonstrated shorter training times compared to the others. The running times are 589.75 and 418.31 ms, respectively. The superior feasibility and reliability of the machine learning algorithm were validated by contrasting with the computation of the empirical formulas. The empirical formulas exhibited higher error values, with the majority of MSE indicators exceeding 50 and the lowest R2 value even presenting negative values. Furthermore, feature importance calculations were used to understand the ability of the machine learning models to perceive each influencing factor. Among them, the corrosion rate, the yield strength of the longitudinal bar, and the stirrup emerged as the three most important influential factors.

Video-based HR measurement using adaptive facial regions with multiple color spaces

Driven by the desire for feasible and convenient healthcare, non-contact heart rate (HR) monitoring based on consumer-grade cameras has gained significant recognition among researchers. However, this technology suffers from performance reliability and consistency in realistic situations of motion artifacts, illumination variations, and skin tones, limiting it to emerge as an alternative to conventional methods. Considering these challenges, this paper suggests an effective technique for HR measurement from facial RGB videos. The face being the region of interest (ROI) is divided into several small sub-ROIs of even size. A group of quality sub-ROIs is formed and weighted based on the fundamental periodicity coefficient to handle spatial non-uniform illumination and facial motions. Five different color spaces are considered, and the most suitable color component from each space is chosen to alleviate the influence of temporal illumination variation and other factors. The resultant color signals are denoised using the ensemble empirical mode decomposition and integrated using the principal component analysis to derive a pulsating component representing the blood volumetric changes for HR computation. Experiments are conducted over three standard datasets, namely PURE, UBFC, and COHFACE. The obtained mean absolute error values are 1.16 beats per minute (bpm), 1.56 bpm, and 2.10 bpm for PURE, UBFC, and COHFACE datasets, respectively, indicating the performance of the technique well above the clinically acceptable threshold. In comparison, the technique showed performance superiority over the state-of-art methods. These outcomes substantiate the potential of alternative color spaces for accurate and reliable HR monitoring from facial videos in challenging scenarios.

Advancing heat transfer modeling through machine learning: A focus on forced convection with nanoparticles

This study underscores the critical role of heat transfer modeling in the design and optimization of heat exchangers, recognizing the challenges posed by traditional numerical methods. To address the need for more efficient alternatives, machine learning emerges as a promising solution, leveraging its capacity to unravel intricate relationships in heat transfer processes. The research focuses on forced convection heat transfer involving nanoparticles (specifically TiO2, Al2O3, and Cu) dispersed in water, employing the Lagrangian–Eulerian approach under turbulent flow conditions with a constant surface heat flux. Machine-learning techniques, namely extreme gradient boosting, support vector regression, and tree regression are employed to predict heat transfer outcomes based on collected data. The achieved results exhibit promising accuracy levels, with eXtreme gradient boosting, support vector regression, and tree regression algorithm models demonstrating impressive accuracies of 91%, 91%, and 94%, respectively. The mean absolute errors for these machine-learning models were recorded as 1.07, 7.24, and 3.74. This research serves as a demonstration of the potential of machine-learning methodologies in modeling complex physical systems, as evidenced by their efficiency in capturing the behavior of nanofluids under various conditions. The obtained accuracy and low mean absolute error values indicate the robustness of these machine-learning models in predicting heat transfer results. The implications of these findings are substantial for the field of heat exchanger design and optimization. The study suggests that machine-learning techniques offer a more efficient and cost-effective approach compared to traditional numerical simulations. By leveraging the capabilities of these models, there is potential for enhanced accuracy and efficiency in the design and optimization processes of heat exchangers, contributing to advancements in thermal engineering.

State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm

AbstractIn response to the issues of traditional backpropagation (BP) neural networks in state of charge (SOC) estimation, including easy convergence to local optima, slow convergence speed, and low accuracy, this paper proposes a novel adaptive crossover mutation strategy and dynamic sparrow search algorithm to optimize BP networks' initial values and thresholds (ACMSSA‐BP). The proposed method is based on the sparrow search algorithm, where the number of producers and scroungers is adjusted through an adaptive factor. This improvement effectively transitions the search process from extensive full exploration to localized fine‐tuning search. In the position update phase of the producers, crossover mutation and dynamic search strategies are introduced to increase the diversity of good populations, prevent the algorithm from converging to local optima, and maintain its local search capability in the later stage. Using real transportation data from coal mining flame‐proof tracked vehicles, we applied correlation theory to extract model feature parameters and constructed a training data set to estimate the SOC. The results of both static and dynamic validation experiments have indicated that the ACMSSA‐BP method has delivered impressive performance in predicting SOC, as reflected in the mean absolute error, root mean squared error, and mean absolute percentage error values of less than 1.5%, 1.5%, and 1.6%, respectively. Compared with BP, SSA‐BP, CMSSA‐BP, PSO‐BP, and NARX_NN methods, the ACMSSA‐BP approach demonstrates enhanced accuracy in estimation, significant robustness, and impressive generalization capabilities.

Reverberation-Robust Self-Calibration and Synchronization of Distributed Microphone Arrays by Mitigating Heteroscedasticity and Outlier Occurrence in TDoA Measurements.

The network of distributed microphone arrays is usually established in an ad hoc manner; hence, network parameters such as the mutual positioning and rotation of the arrays, positions of sources, and synchronization of their recording onset times are initially unknown. In this article, we consider the problem of passively jointly self-calibrating and synchronizing distributed arrays in reverberant rooms. We use a typical two-step approach where, initially, the relative geometry of the network is estimated using Direction of Arrival (DoA) measurements. Subsequently, the absolute scale and synchronization parameters are estimated using Time Difference of Arrival (TDoA) measurements. This article presents methods to improve the robustness and accuracy of estimation of the absolute geometric scaling and synchronization parameters in reverberant conditions, in which TDoA measurements do not follow a normal distribution; furthermore, outliers often occur. To remedy these issues, we propose a Weighted Least Squares (WLS) estimator and schema for weighting the TDoA measurements to increase the estimation accuracy from heteroscedastic TDoA measurements. In addition, we propose an iterative reweighing algorithm with a binary weight to detect and reject TDoA outliers, which exploits the residuals of the parametric model in the least absolute value minimization. A numerical evaluation shows significant improvements in the proposed method over the state of the art in terms of the relative scaling error and mean absolute value of the synchronization parameters.

Characterization of BDS Multipath Effect Based on AT-Conv-LSTM Network

Multipath effects are the most challenging error sources for the Global Navigation Satellite System receiver, affecting observation quality and positioning accuracy. Due to the non-linear and time-varying nature, multipath error is difficult to process. Previous studies used a homogeneous indicator to characterize multipath effects and only revealed the temporal or spatial correlations of the multipath, resulting in limited correction performance. In this study, we consider the code multipath to be influenced not only by the elevation and azimuth angle of certain stations to satellites but also to be related to satellite characteristics such as nadir angle. Hence, azimuth angle, elevation angle, nadir angle and carrier-to-noise power density ratio are taken as multiple indicators to characterize the multipath significantly. Then, we propose an Attention-based Convolutional Long Short-Term Memory (AT-Conv-LSTM) that fully exploits the spatiotemporal correlations of multipath derived from multiple indicators. The main processing procedures using AT-Conv-LSTM are given. Finally, the AT-Conv-LSTM is applied to a station for 16 consecutive days to verify the multipath mitigation effectiveness. Compared with sidereal filtering, multipath hemispherical map (MHM) and trend-surface analysis-based MHM, the experimental results show that using AT-Conv-LSTM can decrease the root mean square error and mean absolute error values of the multipath error more than 60% and 13%, respectively. The proposed method can correct the code multipath to centimeter level, which is one order of magnitude lower than the uncorrected code multipath. Therefore, the proposed AT-Conv-LSTM network could be used as a powerful alternative tool to realize multipath reduction and will be of wide practical value in the fields of standard and high-precision positioning services.

A Wi-Fi dynamic routing algorithm based on energy prediction for wildlife monitoring wireless network

The routing protocol of wildlife monitoring Wi-Fi (Wireless Fidelity) networks cannot balance node energy consumption, leading to early node death. Therefore, the research on energy balance in wildlife monitoring Wi-Fi networks is a hot topic. In order to balance the energy consumption of Wi-Fi networks and extend the lifespan of wireless networks, we designed the low energy dynamic routing protocol LEACH-EP (Low Energy Adaptive Clustering Hierarchy- Energy Prediction) based on energy prediction by analyzing the long-range dependent characteristics of the remaining energy time series (RETS) of wireless network nodes. This protocol uses the LSTM (Long Short-Term Memory) model to predict the remaining energy of network nodes, and then dynamically plans routes using future remaining energy. We conducted a networking experiment in the Anzihe Nature Reserve in Chengdu, China, and the Energy Balance Factor index of the wireless network significantly improved. The Mean Absolute Error value of network nodes is less than 60 mW, which is less than 10% of the average daily energy consumption of nodes. Half of the surviving network nodes have achieved an increase to 55.2%, and the network death time has been extended by 38.6%. The experimental results show that the energy prediction routing protocol LEACH-EP can significantly extend the node survival life and balance network energy consumption.

Effect of isokinetic eccentric training on the human shoulder strength, flexibility, and muscle architecture in physically active men: A preliminary study.

Strengthening the rotator cuff muscles is important for injury prevention and rehabilitation. Since muscle fascicle length improves motor performance and is suggested to reduce the risk of injury for the hamstring, it may be an important variable to promote multidirectional changes in the function and macroscopic structure for the shoulder. Recent literature reviews overwhelmingly suggest that eccentric exercises improve fascicle length and functional measures for the lower limb. However, there is a research gap for the shoulder. Since ultrasound imaging is the most commonly used imaging technique to quantify muscle structure, but has yielded heterogeneous results in different studies, there is another issue and a research gap for the imaging method. Based on the research gaps, the purpose of this study was to evaluate the effects of standardized eccentric strength training on the function and structure of the external rotator cuff muscles using an isokinetic dynamometer and MRI. Therefore, a preliminary pre-post intervention study was conducted and 16 physically active men were recruited in October 2021. For the right shoulder, an eccentric isokinetic training was performed twice a week for almost six weeks. The primary outcome measures (external rotators) were active and passive range of motion, eccentric and concentric torque at 30, 60, and 180°/s isokinetic speed, and fascicle length and fascicle volume for the supraspinatus and infraspinatus muscles. The findings show a training effect for the absolute mean values of eccentric strength (+24%, p = .008). The torque-angle relationship increased, especially in the final phase of range of motion, although a 4% (p = .002) decrease in passive range of motion was found in the stretch test. Positive changes in muscle structure were shown for the supraspinatus muscle fascicle length (+16%, p = .003) and fascicle volume (+19%, p = .002). Based on the study results, we can conclude that eccentric isokinetic training has a significant positive effect on the shoulder. To our knowledge, this is the first eccentric training study using both isokinetic dynamometer and muscle diffusion tensor imaging to access functional and structural changes in the human shoulder rotator cuff muscles. The methods were shown to be applicable for interventional studies. Based on these results, populations such as high-performance handball players with highly trained shoulders should be included in future studies.

Reference exophthalmometric values in south Indian children between 3-7 years old

To provide the reference exophthalmometric values in Indian children aged between 3-7 years.The study consisted of 528 children of both gender (age range from 3-7 years old), who were randomly selected from six schools in the two districts of south India. Normal children free of orbital diseases and other ocular abnormalities were included in the study. Exophthalmometry values were measured in right eye and left eye using a monocular Luedde exophthalmometer in the primary position. The mean absolute exophthalmomeric values among 3-7 years old children in OD and OS were 11.38 ± 0.93 mm (CI: 11.26-11.42) and 11.32 ± 0.93 mm (CI: 11.24-11.40) respectively. The mean absolute exophthalmomeric values of the OD and OS of male children were 11.28 ± 0.93mm and 11.25 ± 0.94 mm respectively. Similarly, for female children mean absolute exophthalmomeric values of the OD and OS were 11.41 ± 0.93mm and 11.39 ± 0.91mm respectively. The range of exophthalmometry values in these group of children is ranging from 9-15.5 mm. There is no significant difference (P=0.35) in exophthalmometry values among male and female children. There is a significant positive correlation between absolute exophthalmomeric values and age (r = 0.62, P &amp;#60; 0.05). None of the children in this study have shown relative exophthalmometry of more than 2mm. In order to diagnose orbital diseases and craniofacial deformities in clinical contexts, ophthalmologists and other healthcare professionals might use the exophthalmomeric values of Indian children aged 3 to 7 presented in this study.