Search Results (30)

Background: Among older adults, there is a high incidence of history of fall (HoF), fear of falling (FoF), and falls on stair descent during gait transitions. Purpose: We aim to evaluate the association between HoF and FoF on spatiotemporal and lower-limb kinematic parameters in older adults during stair descents and gait transitions. Methods: Sixty older adults (>60 years) were evaluated through an optoelectrical motion capture system during stair descents and gait transitions, using the mean value of the task velocity and time; single- and double-support time; peak downward center of mass (CoM) velocity; hip, knee, and ankle positions of ipsi and the contralateral limb; and foot clearance and foot placement, assessed through multivariate analysis of variance. Results: FOF exhibited longer time to complete (p = 0.009) and double-support (p = 0.047) and single-support (p = 0.009) times and a reduced peak downward CoM velocity (p = 0.043). In the gait transition cycle, HOF exhibited reduced ipsi ankle angles at toe-off (p = 0.015), and FOF presented reduced ipsi ankle angles at heel-strike (p = 0.041) and toe-off (p = 0.026) and reduced contralateral ankle angles at toe-off (p = 0.022). Conclusion: Older adults with HoF and FoF exhibit biomechanical changes during stair descents and gait transitions, in line with the use of more conservative strategies to avoid falling. Full article

Human–robot skill transfer enables robots to learn skills from humans and adapt to new task-constrained scenarios. During task execution, robots are expected to react in real-time to unforeseen dynamic obstacles. This paper proposes an integrated human–robot skill transfer strategy with offline task-constrained optimization and real-time whole-body adaptation. Specifically, we develop the via-point trajectory generalization method to learn from only one human demonstration. To incrementally incorporate multiple human skill variations, we encode initial distributions for each skill with Joint Probabilistic Movement Primitives (ProMPs) by generalizing the template trajectory with discrete via-points and deriving corresponding inverse kinematics (IK) solutions. Given initial Joint ProMPs, we develop an effective constrained optimization method to incorporate task constraints in Joint and Cartesian space analytically to a unified probabilistic framework. A double-loop gradient descent-ascent algorithm is performed with the optimized ProMPs directly utilized for task execution. During task execution, we propose an improved real-time adaptive control method for robot whole-body movement adaptation. We develop the Dynamical System Modulation (DSM) method to modulate the robot end-effector through iterations in real-time and improve the real-time null space velocity control method to ensure collision-free joint configurations for the robot non-end-effector. We validate the proposed strategy with a 7-DoF Xarm robot on a series of offline and real-time movement adaptation experiments. Full article

In this paper, we study a recently established s-hybrid approach for generating gradient descent methods for solving optimization tasks. We present an s-hybrid variant of the accelerated double-direction method. The results obtained based on convergence analysis confirm the first-order consistency of the newly defined method on a set of strictly convex quadratic functions. Full article

The Barzilai-Borwein (BB) method usually uses BB stepsize for iteration so as to eliminate the line search step in the steepest descent method. In this paper, we modify the BB stepsize and extend it to solve the optimization problems of three-dimensional quadratic functions. The discussion is divided into two cases. Firstly, we study the case where the coefficient matrix of the quadratic term of quadratic function is a special third-order diagonal matrix and prove that using the new modified stepsize, this case is R-superlinearly convergent. In addition to that, we extend it to n-dimensional case and prove the rate of convergence is R-linear. Secondly, we analyze that the coefficient matrix of the quadratic term of quadratic function is a third-order asymmetric matrix, that is, when the matrix has a double characteristic root and prove the global convergence of this case. The results of numerical experiments show that the modified method is effective for the above two cases. Full article

Background: Several significant associations between certain Human Leukocyte Antigen (HLA) alleles and myasthenia gravis (MG) subtypes were established in populations from Western Europe and North America and, to a lesser extent, from China and Japan. However, such data are scarcely available for Eastern Europe. This study aimed to analyze the associations of HLA Class I and II alleles with MG and its serological subtypes (with anti-acetylcholine receptor autoantibodies, RAch+MG, and double-seronegative, dSNMG) in myasthenic patients of Romanian descent. Methods: We consecutively enrolled adult Romanian unrelated myasthenic patients, which were genotyped by next-generation sequencing for HLA-A, -B, -C, -DRB1 and -DQB1. The descent-matched controls were represented by two separate groups of random normal subjects genotyped for the main five HLA loci at the two-digit and four-digit levels, respectively, collected from the Allele Frequency Net Database. Results: A total of 40 patients (females: 80.00%; median age at onset: 42.5 years, range: 1–78; RAch+MG: 75.00%; dSNMG: 22.50%) were included. We were able to confirm previously acknowledged allelic associations: positive for HLA-B*08, DRB1*14:54 and DRB1*16:01 and negative for DRB1*13. However, we found some potential novel significant positive associations between MG and the HLA-A*02:36, B*47, B*73, B*44:27 and B*57:02 alleles. All alleles positively associated with MG remained significantly associated with RAch+MG, regardless of the patients’ clinical and thymic heterogeneity. We found significant positive associations between dSNMG and the HLA-B*47, B*44:27 and DRB1*14:54 alleles that are shared with RAch+MG. Conclusions: These results suggest both distinct and common etiopathogenic mechanisms between dSNMG and RAch+MG. Our study pioneers allele associations in Romanian MG patients. Full article

Parkinson’s disease (PD) is a neurodegenerative illness that affects the central nervous system and leads to a gradual degeneration of neurons that results in movement slowness, mental health problems, speaking difficulties, etc. In the past 20 years, the frequency of PD has doubled. Global estimates revealed that over 8.5 million cases have been identified so far. Thus, early and accurate detection of PD is crucial for treatment. Traditional detection methods are subjective and prone to delays, as they are reliant on clinical evaluation and imaging. Alternatively, artificial intelligence (AI) has recently emerged as a transformative technology in the healthcare sector, showing decent and promising results. However, an effective algorithm needs to be investigated for the most accurate prediction of a particular disease. Thus, this paper explores the ability of different machine learning algorithms in regard to the effective detection of PD. A total of 26 algorithms were implemented using the Scikit-Learn library on the Oxford PD detection dataset. This is a collection of 195 voice measurements recorded from 31 individuals, of which 23 have PD. The implemented algorithms are logistic regression, decision tree, k-nearest neighbors, random forest, support vector machine, Gaussian naïve bayes, multi-layered perceptron (MLP), extreme gradient boosting, adaptive boosting, stochastic gradient descent, gradient boosting machine, extra tree classifier, light gradient boosting machine, categorical boosting, Bernoulli naïve bayes, complement naïve bayes, multinomial naïve bayes, histogram-based gradient boosting, nearest centroid, radius neighbors classifier, logistic regression with elastic net regularization, extreme learning machine, ridge classifier, huber classifier, perceptron classifier, and voting classifier. Among them, MLP outperformed the other algorithms with a testing accuracy of 95%, precision of 94%, sensitivity of 100%, F1 score of 97%, and AUC of 98%. Thus, it successfully discriminates healthy individuals from those with PD, thereby helping for accurate early detection of PD for new patients using their voice measurements. Full article

For a two-block splitting iterative scheme to solve the complex linear equations system resulting from the complex Helmholtz equation, the iterative form using descent vector and residual vector is formulated. We propose splitting iterative schemes by considering the perpendicular property of consecutive residual vector. The two-block splitting iterative schemes are proven to have absolute convergence, and the residual is minimized at each iteration step. Single and double parameters in the two-block splitting iterative schemes are derived explicitly utilizing the orthogonality condition or the minimality conditions. Some simulations of complex Helmholtz equations are performed to exhibit the performance of the proposed two-block iterative schemes endowed with optimal values of parameters. The primary novelty and major contribution of this paper lies in using the orthogonality condition of residual vectors to optimize the iterative process. The proposed method might fill a gap in the current literature, where existing iterative methods either lack explicit parameter optimization or struggle with high wave numbers and large damping constants in the complex Helmholtz equation. The two-block splitting iterative scheme provides an efficient and convergent solution, even in challenging cases. Full article

Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a Bayesian sequential selection method, akin to Bayesian experimental design, that identifies critically important information within a dataset while ignoring data that are either misleading or bring unnecessary complexity to the surrogate model of choice. Our method improves sample-wise error convergence and eliminates instances where more data lead to worse performance and instabilities of the surrogate model, often termed sample-wise “double descent”. We find these instabilities are a result of the complexity of the underlying map and are linked to extreme events and heavy tails. Our approach has two key features. First, the selection algorithm dynamically couples the chosen model and data. Data is chosen based on its merits towards improving the selected model, rather than being compared strictly against other data. Second, a natural convergence of the method removes the need for dividing the data into training, testing, and validation sets. Instead, the selection metric inherently assesses testing and validation error through global statistics of the model. This ensures that key information is never wasted in testing or validation. The method is applied using both Gaussian process regression and deep neural network surrogate models. Full article

The minimum error entropy (MEE) criterion finds extensive utility across diverse applications, particularly in contexts characterized by non-Gaussian noise. However, its computational demands are notable, and are primarily attributable to the double summation operation involved in calculating the probability density function (PDF) of the error. To address this, our study introduces a novel approach, termed the fast minimum error entropy (FMEE) algorithm, aimed at mitigating computational complexity through the utilization of polynomial expansions of the error PDF. Initially, the PDF approximation of a random variable is derived via the Gram–Charlier expansion. Subsequently, we proceed to ascertain and streamline the entropy of the random variable. Following this, the error entropy inherent to the linear regression model is delineated and expressed as a function of the regression coefficient vector. Lastly, leveraging the gradient descent algorithm, we compute the regression coefficient vector corresponding to the minimum error entropy. Theoretical scrutiny reveals that the time complexity of FMEE stands at $O(n)$, in stark contrast to the $O(n^2)$ complexity associated with MEE. Experimentally, our findings underscore the remarkable efficiency gains afforded by FMEE, with time consumption registering less than 1‰ of that observed with MEE. Encouragingly, this efficiency leap is achieved without compromising accuracy, as evidenced by negligible differentials observed between the accuracies of FMEE and MEE. Furthermore, comprehensive regression experiments on real-world electric datasets in northwest China demonstrate that our FMEE outperforms baseline methods by a clear margin. Full article

The realization of a BRST cohomology of the 4D topological gauge-affine gravity is established in terms of a superconnection formalism. The identification of fields in the quantized theory occurs directly as is usual in terms of superconnection and its supercurvature components with the double covering of the general affine group $\overline{GA}(4,\mathbb{R})$. Then, by means of an appropriate decomposition of the metalinear double-covering group $\overline{SL}(5,\mathbb{R})$ with respect to the general linear double-covering group $\overline{GL}(4,\mathbb{R})$, one can easily obtain the enlargements of the fields while remaining consistent with the BRST algebra. This leads to the descent equations, allowing us to build the observables of the theory by means of the BRST algebra constructed using a $\overline{sa}(5,\mathbb{R})$ algebra-valued superconnection. In particular, we discuss the construction of topological invariants with torsion. Full article

Over recent decades, the hilly and gully regions of the northern Loess Plateau in Shaanxi province have grappled with severe soil erosion and a precarious ecological milieu. Shaped by urbanization policies, this locale has encountered a gamut of issues, including an imbalance in human–environment dynamics and the degradation of ecological integrity. Consequently, the comprehension of how urban expansion impacts the optimization of regional landscape configurations, the alignment of human–environment interactions in the Loess Plateau’s hilly and gully domains, and the mitigation of urban ecological challenges assumes paramount importance. Leveraging data from land use remote sensing monitoring, alongside inputs from natural geography and socio-economic spheres, and employing methodologies such as landscape pattern indices, we conduct an exhaustive analysis of Zichang City’s urban fabric from 1980 to 2020. Furthermore, employing the CLUE-S model, we undertake multifaceted scenario simulations to forecast urban expansion in Zichang City through to 2035. Our findings delineate two distinct phases in Zichang City’s urban expansion trajectory over the past four decades. From 1980 to 2000, urban construction land in Zichang City experienced a phase of methodical and steady growth, augmenting by 64.98 hectares, alongside a marginal decrease in the landscape shape index (LSI) by 0.02 and a commensurate increase in the aggregation index (AI) by 1.17. Conversely, from 2000 to 2020, urban construction land in Zichang City witnessed an epoch of rapid and haphazard expansion, doubling in expanse, marked by a notable escalation in LSI (2.45) and a corresponding descent in the AI (2.85). The precision of CLUE-S model simulations for Zichang City’s land use alterations registers at 0.88, fulfilling the exigent demand for further urban expansion and land use change prognostication. Under the aegis of the natural development scenario, the augmentation of urban construction land in Zichang City primarily encroaches upon grassland, farmland, and woodland, effectuating an increase of 159.81 hectares. Conversely, under the ambit of urbanization development, urban construction land contends predominantly with farmland, grassland, and woodland, heralding an augmentation of 520.42 hectares. Lastly, under the mantle of ecological protection, urban construction land expansion predominantly encroaches upon grassland, farmland, and woodland, resulting in an augmentation of 4.27 hectares. Through a nuanced analysis of the spatiotemporal evolution of urban expansion and scenario-based simulations, this study endeavors to furnish multi-faceted, scenario-driven, and policy-centric insights for regional planning, urban spatial delineation, and regional ecological safeguarding. Full article

Path planning plays a crucial role in the execution of pursuit tasks for multiple unmanned ground vehicles (multi-UGVs). Although existing popular path-planning methods can achieve the pursuit goals, they suffer from some drawbacks such as long computation time and excessive path inflection points. To address these issues, this paper combines gradient descent and deep reinforcement learning (DRL) to solve the problem of excessive path inflection points from a path-smoothing perspective. In addition, the prioritized experience replay (PER) method is incorporated to enhance the learning efficiency of DRL. By doing so, the proposed model integrates PER, gradient descent, and a multiple-agent double deep Q-learning network (PER-GDMADDQN) to enable the path planning and obstacle avoidance capabilities of multi-UGVs. Experimental results demonstrate that the proposed PER-GDMADDQN yields superior performance in the pursuit problem of multi-UGVs, where the training speed and smoothness of the proposed method outperform other popular algorithms. As a result, the proposed method enables satisfactory path planning for multi-UGVs. Full article

The structures of Ag complexes with dimethyl amino phenyl substituted phthalocyanine ^m[dmaphPcAg]^q of various charges q and in the two lowest spin states m were optimized using the B3LYP method within the D_4h symmetry group and its subgroups. The most stable reaction intermediate in the supposed photoinitiation reaction is ³[dmaphPcAg]⁻. Group-theoretical analysis of the optimized structures and of their electron states reveals two symmetry-descent mechanisms. The stable structures of maximal symmetry of complexes ¹[dmaphPcAg]⁺, ³[dmaphPcAg]⁺, ²[dmaphPcAg]⁰, and ⁴[dmaphPcAg]²⁻ correspond to the D₄ group as a consequence of the pseudo-Jahn–Teller effect within unstable D_4h structure. Complexes ⁴[dmaphPcAg]⁰, ¹[dmaphPcAg]⁻, ³[dmaphPcAg]⁻, and ²[dmaphPcAg]²⁻ with double degenerate electron ground states in D_4h symmetry structures undergo a symmetry descent to stable structures corresponding to maximal D₂ symmetry, not because of a simple Jahn–Teller effect but due to a hidden pseudo-Jahn–Teller effect (strong vibronic interaction between excited electron states). The reduction of the neutral photoinitiator causes symmetry descent to its anionic intermediate because of vibronic interactions that must significantly affect the polymerization reactions. Full article

The generalized penalized constrained regression (G-PCR) is a penalized model for high-dimensional linear inverse problems with structured features. This paper presents a sharp error performance analysis of the G-PCR in the over-parameterized high-dimensional setting. The analysis is carried out under the assumption of a noisy or erroneous Gaussian features matrix. To assess the performance of the G-PCR problem, the study employs multiple metrics such as prediction risk, cosine similarity, and the probabilities of misdetection and false alarm. These metrics offer valuable insights into the accuracy and reliability of the G-PCR model under different circumstances. Furthermore, the derived results are specialized and applied to well-known instances of G-PCR, including ${\mathcal{l}}_1$-norm penalized regression for sparse signal recovery and ${\mathcal{l}}_2$-norm (ridge) penalization. These specific instances are widely utilized in regression analysis for purposes such as feature selection and model regularization. To validate the obtained results, the paper provides numerical simulations conducted on both real-world and synthetic datasets. Using extensive simulations, we show the universality and robustness of the results of this work to the assumed Gaussian distribution of the features matrix. We empirically investigate the so-called double descent phenomenon and show how optimal selection of the hyper-parameters of the G-PCR can help mitigate this phenomenon. The derived expressions and insights from this study can be utilized to optimally select the hyper-parameters of the G-PCR. By leveraging these findings, one can make well-informed decisions regarding the configuration and fine-tuning of the G-PCR model, taking into consideration the specific problem at hand as well as the presence of noisy features in the high-dimensional setting. Full article

This research proposes and investigates some improvements in gradient descent iterations that can be applied for solving system of nonlinear equations (SNE). In the available literature, such methods are termed improved gradient descent methods. We use verified advantages of various accelerated double direction and double step size gradient methods in solving single scalar equations. Our strategy is to control the speed of the convergence of gradient methods through the step size value defined using more parameters. As a result, efficient minimization schemes for solving SNE are introduced. Linear global convergence of the proposed iterative method is confirmed by theoretical analysis under standard assumptions. Numerical experiments confirm the significant computational efficiency of proposed methods compared to traditional gradient descent methods for solving SNE. Full article