Number Of Variables Research Articles

An Approach for Evaluation and Recognition of Facial Emotions Using EMG Signal

Background: Facial electromyography (fEMG) records muscular activities from the facial muscles, which provides details regarding facial muscle stimulation patterns in experimentation. Objectives: The Principal Component Analysis (PCA) is mostly implemented, whereas the actual or unprocessed initial fEMG data are rendered into low-spatial units with minimizing the level of data repetition. Methods: Facial EMG signal was acquired by using the instrument BIOPAC MP150. Four electrodes were fixed on the face of each participant for capturing the four different emotions like happiness, anger, sad and fear. Two electrodes were placed on arm for grounding purposes. Results: The aim of this research paper is to propagate the functioning of PCA in synchrony with the subjective fEMG analysis and to give a thorough apprehension of the advanced PCA in the areas of machine learning. It describes its arithmetical characteristics, while PCA is estimated by implying the covariance matrix. Datasets which are larger in size are progressively universal and their interpretation often becomes complex or tough. So, it is necessary to minimize the number of variables and elucidate linear compositions of the data to explicate it on a huge number of variables with a relevant approach. Therefore, Principal Component Analysis (PCA) is applied because it is an unsupervised training method that utilizes advanced statistical concept to minimize the dimensionality of huge datasets. Conclusion: This work is furthermore inclined toward the analysis of fEMG signals acquired for four different facial expressions using Analysis of Variance (ANOVA) to provide clarity on the variation of features.

A Personalised Approach to Identifying Important Determinants of Well-being

Abstract Purpose To develop effective and personalized interventions, it is essential to identify the most critical processes or psychological drivers that impact an individual’s well-being. Some processes may be universally beneficial to well-being across many contexts and people, while others may only be beneficial to certain individuals in specific contexts. Method We conducted three intensive daily diary studies, each with more than 50 within-person measurement occasions, across three data sets (n1 = 44; n2 = 37; n3 = 141). We aimed to investigate individual differences in the strength of within-person associations between three distinct process measures and a variety of outcomes. We utilized a unique idiographic algorithm, known as i-ARIMAX (Autoregressive Integrated Moving Average), to determine the strength of the relationship (Beta) between each process and outcome within individuals (“i”). All of the computed betas were then subjected to meta-analyses, with individuals treated as the “study”. Results The results revealed that the process-outcome links varied significantly between individuals, surpassing the homogeneity typically seen in meta-analyses of studies. Although several processes showed group-level effects, no process was found to be universally beneficial when considered individually. For instance, processes involving social behavior, like being assertive, did not demonstrate any group-level links to loneliness but still had significant individual-level effects that varied from positive to negative. Discussion Using i-ARIMAX might help reduce the number of candidate variables for complex within-person analyses. Additionally, the size and pattern of i-ARIMAX betas could prove useful in guiding personalized interventions.

Serum ferritin levels in children of simple febrile seizures in tertiary institute

Background: Seizure onset is impacted by a number of variables, including genetics, diet, geography, concurrent illnesses, metabolic state, history of head trauma, and blood levels of particular minerals. Due to the fact that iron is required for the proper functioning of a number of enzymes and neurotransmitters in the central nervous system, low blood ferritin levels may lower the seizure threshold. The aim of the study was to determine the association between serum ferritin levels and simple febrile seizures. Methods: The present observational study was conducted in department of pediatrics of a tertiary institute and included a total of 200 study subjects who visited to pediatric OPD/emergency room with febrile illness. The data was collected with the help of a structured clinical proforma. The collected data was recorded in Microsoft Excel sheet and statistical analysis was done with the help of SPSS version 21.0. Results: In our study, the maximum number of the study subjects were between 13 and 24 months (35%) with male to female ratio 1.2:1. In patients with simple febrile seizure the mean Hb level was 9.1±1.4 gm/dl, the mean MCV was 72.6±7.4 f1, mean MCH was 23.73±3.2 pg and mean serum ferritin was 13.4±9.5 ng/ml as compared to those patients with febrile illness without seizure the mean Hb level was 11.5±1.2 gm/dl, mean MCV was 82.1±5.5 f1, mean MCH was 29.6±2.9 pg and mean serum ferritin was 33.6±20.2 ng/ml. Conclusions: It is concluded that the low serum ferritin level is a risk factor of simple febrile seizure.

Does Elevated Mean-Arterial Pressure Lead to Better Outcomes in Degenerative Cervical Myelopathy?- A Prospective, Pilot Randomized Control Trial.

Randomized Control Trial. DCM refers to compression of spinal cord either due to static/dynamic causes or commonly, a result of combination of both. Number of variables exist, which determine prognosis post-surgery. Role of intra-operative blood pressure has not been analyzed in depth in current literature. Elevating MAP post SCI is widely practiced and forms a recommendation of AANS/CNS Joint Committee Guidelines. This led us to investigate role played by elevated MAP during surgery for DCM, in order to optimize outcomes. This prospective randomized comparative pilot study was conducted at a tertiary care spine centre. 84 patients were randomly divided in two groups. Group 1 had intra-operative MAP in normal range. Group 2, had intra-operative BP 20 mmHg higher than preoperative average MAP with a variation of + 5mmHg. Outcomes were recorded at 3 months, 6 months and 1 year by mJOA, VAS and ASIA scale. Neurological improvement was documented in 19/30 (63.3%) patients of hypertensive group compared to 16/30 (53.3%) patients of normotensive group. Improvements in mJOA scores were better for hypertensive group during the 1-year follow-up. Improvement in VAS scores were comparable between two groups, but at 1-year follow-up the VAS score of hypertensive groups was significantly lower. MAP should be individualized according to preoperative average blood pressure assessment of patient. Keeping intraoperative MAP at higher level (preoperative MAP + 20mmHg) during surgery for DCM can result in better outcomes.

FIRE 6.5: Feynman integral reduction with new simplification library

FIRE is a program which performs integration-by-parts (IBP) reductions of Feynman integrals. Originally, the C++ version of FIRE relies on the computer algebra system Fermat by Robert Lewis to simplify rational functions. We present an upgrade of FIRE which incorporates a new library FUEL initially described in a separate publication, which enables a flexible choice of third-party computer algebra systems as simplifiers, as well as efficient communications with some of the simplifiers as C++ libraries rather than through Unix pipes. We achieve significant speedups for IBP reductions of Feynman integrals involving many kinematic variables, when using an open source backend based on FLINT newly added in this work, or the Symbolica backend developed by Ben Ruijl as a potential successor of FORM. Program summaryProgram title: FIRE, version 6.5 (FIRE 6.5)CPC Library link to program files:https://doi.org/10.17632/cy6k69pb3y.2Developer's repository link:https://gitlab.com/feynmanintegrals/fire.gitLicensing provisions: GPLv2Programming language:Wolfram Mathematica 8.0 or higher, C++17Supplementary material: See linked repository for installation instructions.Journal reference of previous version: Comput. Phys. Commun. 247 (2020) 106877Does the new version supersede the previous version?: Yes.Reasons for the new version: The new version no longer relies on a single computer algebra system, Fermat [1], but instead allows a flexible choice of several systems, some of which offer higher performance, especially when the number of variables is large.Summary of revisions: A new library FUEL [2] is used as a core component of the new version of FIRE to access different computer algebra systems as simplifiers of rational function expressions. Since the first release of FUEL described elsewhere, FUEL version 1.0 here has been enhanced with a new backend based on the open source library FLINT [3] that provides highly performant simplification of rational functions.Nature of problem: Feynman integrals of a given family are reduced to a finite set of master integrals, by solving linear equations arising from integration by parts, using Gaussian elimination. The coefficients of the linear equations are generally rational functions in kinematic variables and the spacetime dimension, and the simplification of such rational functions during Gaussian elimination is a key task that is improved in this upgrade of FIRE.Solution method: Computer algebra systems with state-of-the-art capabilities for polynomial GCD computations are used as simplification backends, or simplifiers in short. Due to the design of FIRE, text strings are used as the exchange format for rational functions before and after simplification. A fast C++ parser is written to parse strings into the internal format of an external simplifer, FLINT [3], with state-of-the-art performance for multivariate polynomial calculations. Similarly, the simplifier Symbolica [4] has high performance in both GCD computations and parsing, and has been integrated into FIRE.

Application of artificial intelligence in predicting major arrhythmic events based on clinical and left ventricular electro-anatomical mapping data

Abstract Introduction Artificial intelligence (AI) is a ground-breaking frontier in the analysis of intricate and extensive datasets, also within the medical domain. AI can be used to meticulously and systematically analyse data derived from electro-anatomical mapping, scrutinizing the numerical data extracted from the mapping engineering files. Purpose The primary objective of the project is to utilize machine learning methods to identify potential correlations between clinical, procedural, and substrate-mapping variables with major arrhythmic cardiac events (MaCE). Methods Machine learning algorithms were implemented using a database of 220 patients who underwent endocardial left ventricular electro-anatomical mapping (EAM). A total of 223 clinical-procedural variables were collected (analysis conducted for the 61 most interesting variables) and substrate mapping engineering files/data were extrapolated for analysis. Combinations of 4 and 5 variables are explored, resulting in 487,635 and 5,461,512 combinations, respectively. All the results obtained from all the combinations and different methods were evaluated by calculating the AUC (parameter indicating the probability that the prediction is in the correct order). For each patient, the numerical data extracted from the EAM files were assessed, specifically focusing on: 1. The late potentials extension and dispersion (%LatArea). 2. Point-to-point difference of the bipolar potential compared to the unipolar potential, with identification of regions where multiple points with high differences are clustered (%UniBip) 3. Areas of deceleration, characterized by very early and very late potentials within a short range, indicating a substantial difference in the activation timing (GradVal). Results Analyzing the best six 4-variable logistic regression models, we note that the variables that are most frequently correlated with a MaCE during follow-up are "NYHA class", "PAPs" and "GradVal" (AUC of the best models was: 0,96; 0,94; 0,91; 0,90; 0,90; 0,90). Analyzing the best six 5-variable logistic regression models emerges that the variables most frequently correlated with a MaCE during follow-up are "NYHA class", "PAPs", "GradVal", "Arrhythmic Storm", "TAPSE" and "%UniBip" (AUC of the best models was: 0,97; 0,93; 0,92; 0,92; 0,92; 0,92). EAM data added to clinical data increases the number of regression variables and the model complexity; "GradVal" and "%UniBip" seem to have greater relevance. Conclusion The study successfully employs machine learning algorithms to identify key variables influencing the prediction of MaCE. The analysis of EAM numerical data through AI can allow for the stratification of MaCE risk. The application of artificial intelligence allows to analyse a large amount of computer data that would not be analysable by human capabilities alone. Ongoing refinement and expansion of the dataset can enhance the models' predictive capabilities for clinical applications.

ENTREPRENEURIAL ORIENTATION AS AN ANTECEDENT TO THE RAPID INTERNATIONALIZATION OF MOROCCAN SMES IN THE WOMEN’S ENTREPRENEURSHIP FIELD

Purpose – This article aims to explore the importance of the international entrepreneurial orientation (IOE) of Moroccan women entrepreneurs (based on innovation, risk-taking and proactivity) in the speed of internationalization of Moroccan SMEs through the niche strategy as an intermediate variable. Research methodology – We adopted a quantitative study on a sample of 45 Moroccan SMEs to test the research hypotheses using the structural equation method, under Smart-PLS software. Findings – A significant relationship between the international entrepreneurial orientation of Moroccan women entrepreneurs and the speed of internationalization with the intermediation of the niche strategy. Despite well-calculated steps, innovative products and the courage to take risks on foreign markets, women entrepreneurs tend to adopt niche strategies to be more protected in the internationalization process: both to avoid direct competition from large companies and to speed up the internationalization process Research. Limitations – Theoretically, it was difficult to operationalize the phenomenon studied, for several reasons: the large number of movements in this approach, the number of variables mobilized. Methodologically, the criteria used to select the sample made it difficult to find more than 45 eligible companies (Exploratory quantitative study). Originality/Value – This article is one of the very few to combine international and women’s entrepreneurship. Furthermore, this is the first article in the field of women’s international entrepreneurship, as far as I know, that addresses the relationship between entrepreneurial orientation and the speed of internationalization of exporting SMEs.

Lie group analysis of bingham plastic flow over a straining surface in the presence of magnetohydrodynamic effects and slip conditions

This paper investigates the two-dimensional flow of a Bingham plastic over a straining surface subjected to an externally applied magnetic field and surface slips. The study aims to understand the behavior of such flows and their response to external factors, which has applications in various industrial processes involving complex fluid dynamics. Through Lie group analysis, a new set of similarity transformations are derived to reduce the number of variables in the governing partial differential equations, facilitating a more tractable analysis. These transformations enable the conversion of the partial differential equations into a self-similar system of ordinary differential equations. A high-order, three-stage Lobatto IIIa formula along with appropriate boundary conditions is applied to solve this system. The solutions obtained for various physical parameters lead to several key deductions. It is found that under constant physical parameter values, the velocity layer thickness of the plastic flow is lower compared to the thermal layer thickness, indicating the dominance of the plastic flow behavior. Additionally, an increase in the magnetic field results in a reduction in the thickness of the plastic boundary layer, highlighting the significant influence of magnetic fields on the flow characteristics. These findings provide valuable insights into the control and optimization of processes involving Bingham plastic flows, particularly in the presence of magnetic fields and surface slips.

Hitting Sets for Orbits of Circuit Classes and Polynomial Families

The orbit of an n -variate polynomial f ( x ) over a field \({\mathbb {F}} \) is the set \(\text {orb}(f) := \lbrace f(A{\mathbf {x}}+{\mathbf {b}}) : A \in {\mathrm{GL}}(n,{\mathbb {F}}) \text{ and } {\mathbf {b}} \in {\mathbb {F}}^n\rbrace \) . The orbit of a polynomial f is a geometrically interesting subset of the set of affine projections of f . Affine projections of polynomials computable by seemingly weak circuit classes can be quite powerful. For example, the polynomial \(\mathsf {IMM}_{3,d} \) – the (1, 1)-th entry of a product of d generic 3 × 3 matrices – is computable by a constant-width read-once oblivious algebraic branching program (ROABP), yet every polynomial computable by a size- s general arithmetic formula is an affine projection of \(\mathsf {IMM}_{3,\text {poly}(s)} \) as shown by Ben-or and Cleve [12]. To our knowledge, no efficient hitting set construction was known for \(\text {orb}(\mathsf {IMM}_{3, d}) \) before this work. In this paper, we initiate the study of explicit hitting sets for the orbits of polynomials computable by several natural and well-studied circuit classes and polynomial families. In particular, we give quasi-polynomial time hitting sets for the orbits of: (1) Low-individual-degree polynomials computable by commutative ROABPs . This implies quasi-polynomial time hitting sets for the orbits of the elementary symmetric polynomials and the orbits of multilinear sparse polynomials . (2) Multilinear polynomials computable by constant-width ROABPs . This implies a quasi-polynomial time hitting set for the orbits of the family \(\lbrace \mathsf {IMM}_{3,d}\rbrace _{d \in {\mathbb {N}}} \) . (3) Polynomials computable by constant-depth, constant-occur formulas . This implies quasi-polynomial time hitting sets for the orbits of multilinear depth-4 circuits with constant top fan-in , and also polynomial-time hitting sets for the orbits of the power symmetric polynomials and the sum-product polynomials . (4) Polynomials computable by occur-once formulas . We say a polynomial has low individual degree if the degree of every variable in the polynomial is at most \(\text {poly}(\log n) \) , where n is the number of variables. The first two results are obtained by building upon and strengthening the rank concentration by translation technique of Agrawal, Saha, and Saxena [6]; the second result additionally uses the merge-and-reduce idea from Forbes and Shpilka [30], Forbes, Shpilka, and Saptharishi [27]. The proof of the third result adapts the algebraic independence based technique of Agrawal, Saha, Saptharishi, and Saxena [5], Beecken, Mittmann, and Saxena [11] to reduce to the case of constructing hitting sets for the orbits of sparse polynomials. A similar reduction using the Shpilka-Volkovich (SV) generator based argument in Shpilka and Volkovich [90] yields the fourth result. The SV generator plays an important role in all the four results.

Developing a Model to Predict Self-Reported Student Performance during Online Education Based on the Acoustic Environment

In recent years, great developments in online university education have been observed, favored by advances in ICT. There are numerous studies on the perception of academic performance in online classes, influenced by aspects of a very diverse nature; however, the acoustic environment of students at home, which can certainly affect the performance of academic activities, has barely been evaluated. This study assesses the influence of the home acoustic environment on students’ self-reported academic performance. This assessment is performed by calculating prediction models using the Recursive Feature Elimination method with 40 initial features and the following classifiers: Random Forest, Gradient Boosting, and Support Vector Machine. The optimal number of predictors and their relative importance were also evaluated. The performance of the models was assessed by metrics such as the accuracy and the area under the receiver operating characteristic curve (ROC_AUC-score). The model with the smallest optimal number of features (with 14 predictors, 9 of them about the perceived acoustic environment) and the best performance achieves an accuracy of 0.7794; furthermore, the maximum difference for the same algorithm between using 33 and 14 predictors is 0.03. Consequently, for simplicity and the ease of interpretation, models with a reduced number of variables are preferred.

Comparison Analysis of Punching Buildings With Flat Plate Under Static and Seismic Loads

The structures with the same frame system (flat plate), but with different spans (6 m and 7 m) and slab thicknesses (20 cm, 22 cm, 25 cm and 40 cm), static and seismic calculation was carried out by the finite element method using “LIRA SAPR”and “ETABS” software packages. Design of structure in accordance with Armenian building codes were implemented. In order to analyze the results of calculations, comparative graphs and tables with a number of variables were built. From the results of the analysis it became clear that the average value of punching shear is 14,58 %, and the average difference of the shear reinforcement area is 38,7%. Consequently, the anchorage length also should be checked by the American building standards for structure design.

An Incremental Interpolation Scheme With Discrete Cosine Series Expansion for Multimaterial Topology Optimization

Abstract Topology optimization is a powerful tool for structural design, while its computational cost is quite high due to the large number of design variables, especially for multilateral systems. Herein, an incremental interpolation approach with discrete cosine series expansion (DCSE) is established for multilateral topology optimization. A step function with shape coefficients (i.e., ensuring that no extra variables are required as the number of materials increases) and the use of the DCSE together reduces the number of variables (e.g., from 8400 to 120 for the optimization of the clamped–clamped beam with four materials). Remarkably, the proposed approach can effectively bypass the checkerboard problem without using any filter. The enhanced computational efficiency (e.g., a ∼89.2% reduction in computation time from 439.1 s to 47.4 s) of the proposed approach is validated via both 2D and 3D numerical cases.

Unraveling the dynamic slowdown in supercooled water: The role of dynamic disorder in jump motions.

When a liquid is rapidly cooled below its melting point without inducing crystallization, its dynamics slow down significantly without noticeable structural changes. Elucidating the origin of this slowdown has been a long-standing challenge. Here, we report a theoretical investigation into the mechanism of the dynamic slowdown in supercooled water, a ubiquitous yet extraordinary substance characterized by various anomalous properties arising from local density fluctuations. Using molecular dynamics simulations, we found that the jump dynamics, which are elementary structural change processes, deviate from Poisson statistics with decreasing temperature. This deviation is attributed to slow variables competing with the jump motions, i.e., dynamic disorder. The present analysis of the dynamic disorder showed that the primary slow variable is the displacement of the fourth nearest oxygen atom of a jumping molecule, which occurs in an environment created by the fluctuations of molecules outside the first hydration shell. As the temperature decreases, the jump dynamics become slow and intermittent. These intermittent dynamics are attributed to the prolonged trapping of jumping molecules within extended and stable low-density domains. As the temperature continues to decrease, the number of slow variables increases due to the increased cooperative motions. Consequently, the jump dynamics proceed in a higher-dimensional space consisting of multiple slow variables, becoming slower and more intermittent. It is then conceivable that with further decreasing temperature, the slowing and intermittency of the jump dynamics intensify, eventually culminating in a glass transition.

MINIMIZING TEMPERATURE DEVIATIONS IN RUBBER MIXING PROCESS BY USING ARTIFICIAL NEURAL NETWORKS

ABSTRACT Rubber mixing is a complex manufacturing process that poses challenges for process control due to the high number of control variables, including mixing parameter settings, rheological behavior, compound viscosity, and batch-dependent material variations. Already small deviations from the control variables can influence the compound properties, leading to increased scrap rates. To address these challenges, this paper introduces an artificial intelligence–based approach to enhance process control in rubber mixing by predicting mixing temperatures from input variables. The proposed method uses feedforward neural networks (FFNs) to enable early identification of batch-specific temperature deviations, thereby enabling systematic improvements with each new application. The FFN was trained on a diverse dataset encompassing various rubber recipes and batches. Post-training, the FFN demonstrated remarkable accuracy, achieving a mean absolute percentage error of 1.00% on the training dataset and 1.44% on the validation dataset, thereby showcasing its efficacy in predicting temperature fluctuations within the mixing process. Consequently, the FFN can determine the relevant input variables necessary to achieve specific mixing temperatures, providing a foundation for an automated control system in rubber mixing process. This paper outlines the system architecture of the FFN tailored for rubber mixing and provides a comprehensive overview of the experimental results.

Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery

Forest structural parameters are crucial for assessing ecological functions and forest quality. To improve the accuracy of estimating these parameters, various approaches based on remote sensing platforms have been employed. Although remote sensing yields high prediction accuracy in uniform, even-aged, simply structured forests, it struggles in complex structures, where accurately predicting forest structural parameters remains a significant challenge. Recent advancements in unmanned aerial vehicle (UAV) photogrammetry have opened new avenues for the accurate estimation of forest structural parameters. However, many studies have relied on a limited set of remote sensing metrics, despite the fact that selecting appropriate metrics as powerful explanatory variables and applying diverse models are essential for achieving high estimation accuracy. In this study, high-resolution RGB imagery from DJI Matrice 300 real-time kinematics was utilized to estimate forest structural parameters in a mixed conifer–broadleaf forest at the University of Tokyo Hokkaido Forest (Hokkaido, Japan). Structural and textual metrics were extracted from canopy height models, and spectral metrics were extracted from orthomosaics. Using random forest and multiple linear regression models, we achieved relatively high estimation accuracy for dominant tree height, mean tree diameter at breast height, basal area, mean stand volume, stem density, and broadleaf ratio. Including a large number of explanatory variables proved advantageous in this complex forest, as its structure is influenced by numerous factors. Our results will aid foresters in predicting forest structural parameters using UAV photogrammetry, thereby contributing to sustainable forest management.

Cognitive skills and economic growth in the twenty-first century: Evidence from PISA and cognitive ability studies

In recent years, some educational researchers have questioned the theory of a robust relationship between test scores in international large-scale assessments such as PISA and lagged economic growth. However, both the proponents and opponents of such a human capital approach have mainly focused on earlier growth patterns during the twentieth century. Hence, the current article analysed the relationship between PISA 2000 and economic growth in 2010–2019 for 31 countries, and composite cognitive skills for 80 countries and their relationships with economic growth in 2010–2019. The results indicate that PISA scores or other cognitive skill measures could to some extent predict economic growth between 2010 and 2019, even when controlling for a significant number of variables. In a consecutive analysis, the relations were substantially associated with Middle Eastern countries because many countries from this region experienced political instability and decreasing oil revenues throughout this period. Hence, it is possible that regional and contextual relationships affect these patterns.

Higher rank (𝑞,𝑡)-Catalan polynomials, affine Springer fibers, and a finite rational shuffle theorem

We introduce the higher rank ( q , t ) (q,t) -Catalan polynomials and prove they equal truncations of the Hikita polynomial to a finite number of variables. Using affine compositions and a certain standardization map, we define a d i n v \mathtt {dinv} statistic on rank r r semistandard ( m , n ) (m,n) -parking functions and prove c o d i n v \mathtt {codinv} counts the dimension of an affine space in an affine paving of a parabolic affine Springer fiber. Combining these results, we give a finite analogue of the Rational Shuffle Theorem in the context of double affine Hecke algebras. Lastly, we also give a Bizley-type formula for the higher rank Catalan numbers in the non-coprime case.

Effects of IMU sensor-to-segment calibration on clinical 3D elbow joint angles estimation

Introduction: Inertial Measurement Units (IMU) require a sensor-to-segment calibration procedure in order to compute anatomically accurate joint angles and, thereby, be employed in healthcare and rehabilitation. Research literature proposes several algorithms to address this issue. However, determining an optimal calibration procedure is challenging due to the large number of variables that affect elbow joint angle accuracy, including 3D joint axis, movement performed, complex anatomy, and notable skin artefacts. Therefore, this paper aims to compare three types of calibration techniques against an optical motion capture reference system during several movement tasks to provide recommendations on the most suitable calibration for the elbow joint.Methods: Thirteen healthy subjects were instrumented with IMU sensors and optical marker clusters. Each participant performed a series of static poses and movements to calibrate the instruments and, subsequently, performed single-plane and multi-joint tasks. The metrics used to evaluate joint angle accuracy are Range of Motion (ROM) error, Root Mean Squared Error (RMSE), and offset. We performed a three-way RM ANOVA to evaluate the effect of joint axis and movement task on three calibration techniques: N-Pose (NP), Functional Calibration (FC) and Manual Alignment (MA).Results: Despite small effect sizes in ROM Error, NP displayed the least precision among calibrations due to interquartile ranges as large as 24.6°. RMSE showed significant differences among calibrations and a large effect size where MA performed best (RMSE = 6.3°) and was comparable with FC (RMSE = 7.2°). Offset showed a large effect size in the calibration*axes interaction where FC and MA performed similarly.Conclusion: Therefore, we recommend MA as the preferred calibration method for the elbow joint due to its simplicity and ease of use. Alternatively, FC can be a valid option when the wearer is unable to hold a predetermined posture.

Does larger prostate size provide protection for cancer specific outcomes in localized prostate cancer.

Benign prostatic hyperplasia is common in the aging population and frequently comorbid with localized prostate cancer. Large prostate volume places significant challenges in robotic prostatectomy including reduced mobility and visualization. The goal of this study is to evaluate the effect of prostate volume as a continuous variable on cancer specific outcomes. Three thousand four hundred and twenty five patients with localized prostate cancer at a single institution who underwent robotic prostatectomy were retrospectively reviewed. A number of preoperative, operative, and postoperative variables were collected to evaluate cancer specific outcomes including pathologic stage, tissue margins, and biochemical recurrence (BCR). Logistic regression models and univariate and multivariate analyses were implemented for pathologic stage T3 and BCR respectively. The median follow up time was 52 months (IQR 18-95). 37.4% of the patients had a final pathologic stage of T3 or higher, 21.2% experienced positive surgical margins, and 24.7% of patients experienced BCR. Prostate size was a significant predictor of all three outcomes of interest. Increasing prostate size was protective against both higher pathologic stage and positive surgical margins (odds ratio = 0.989, 0.990 respectively, p < 0.001). There was a modest increase in the risk of BCR with increasing gland size (hazard ratio = 1.006, p < 0.001). These results were most significant for patients with Gleason Grade Groups 1 and 2 prostate cancer. Prostate size is a commonly determined clinical factor that effects both surgical planning and cancer specific outcomes. Increasing prostate size may offer protection against higher stage disease and positive surgical margins. While surgically challenging, favorable oncologic outcomes can be consistently achieved for patients with low-intermediate risk disease.

Spatiotemporal Variation, Meteorological Driving Factors, and Statistical Models Study of Lake Surface Area in the Yellow River Basin

The surface area changes of 151 natural lakes over 37 months in the Yellow River Basin, based on remote sensing data and 21 meteorological indicators, employing spatial distribution feature analysis, principal component analysis (PCA), correlation analysis, and multiple regression analysis, identify key meteorological factors influencing these variations and their interrelationships. During the study period, lake area averages were from 0.009 km2 to 506.497 km2, with standard deviations ranging from 0.003 km2 to 184.372 km2. The coefficient of variation spans from 3.043 to 217.436, indicating considerable variability in lake area stability. Six primary meteorological factors were determined to have a significant impact on lake surface area fluctuations: 24 h precipitation, maximum daily precipitation, hours of sunshine, maximum wind speed, minimum relative humidity, and lakes in the source region of the Yellow River generally showed a significant positive correlation. For maximum wind speed (m/s), 28 lakes showed significant correlations, with five positive and twenty-three negative correlations, correlation coefficients ranging from −0.34 to −0.63, average −0.47, indicating an overall negative correlation between lake surface area and maximum wind speed. For maximum daily precipitation (mm), 36 lakes had 21 showing a positive correlation, indicating a positive correlation between lake surface area and daily precipitation in larger lakes. Furthermore, of the 117 lakes with sufficient data to model, the predictive capabilities of various models for lake surface area changes showcased distinct advantages, with the random forest model outperforming others in a dataset of 65 lakes, Ridge regression is best for 28 lakes, Lasso regression performs best for 20 lakes, Linear model is only best for 4 cases. The random forest model provides the best fit due to its ability to handle a large number of feature variables and consider their interactions, thereby offering the best fitting effect. These insights are crucial for understanding the influence of meteorological factors on lake surface area changes within the Yellow River Basin and are instrumental in developing predictive models based on meteorological data.