Unknown Point Research Articles

Case report: A novel transcutaneous electrical nerve stimulation improves dysesthesias and motor behaviors after transverse myelitis

PurposeTransverse myelitis (TM)-associated dysesthesia is diverse and frequently resistant to treatment. This study explored the comprehensive effects of a novel transcutaneous electrical nerve stimulation (TENS) approach tailored to an individual’s specific dysesthesia profile in a patient with TM.Patient and methodA 52-year-old woman with severe dysesthesias in the left C8 sensory area caused by TM underwent an A-B-A-B-A design intervention. The baseline (phase A) and intervention (phase B) phases were both 7 days. Tingling and allodynia were scored on an 11-point numerical rating scale pre-, post-, and 1 h post-treatment. Upper limb activities during daily living were assessed using a wrist-worn accelerometer. The intervention phase consisted of 60-min sessions of TENS two times daily. Furthermore, the intervention and carry-over effects of TENS were evaluated using Tau-U and Bayesian unknown change point models.ResultsThe effects of TENS resulted in the immediate improvement in tingling, allodynia, and upper limb activity. Long-term effects of TENS affected tingling and upper limb activity; however, no impacts on allodynia were observed.ConclusionThis novel TENS approach shows promise as an effective treatment, even in rare and treatment-resistant dysesthesia associated with TM.

Pergamos. An unexplored fortified site in north-eastern Greece

The aim of this article is to present the first results of a project investigating the hitherto unexplored site of Pergamos, located on a hill known as Koules/Alonaki just to the south of modern Moustheni in the Pieria valley. The site has a long history, with the earliest traces of human activity dating to the Late Bronze Age and the first visible architectural remains—impressive and well-preserved fortifications built in a stack-work technique—originating in the Late Archaic period. Following this, surface finds testify to activity and habitation throughout the centuries until a substantial strengthening of the defensive walls during Late Roman or medieval times, before the site was abandoned at a currently unknown point. To achieve the aim the article begins with an introduction to the site and its surrounding landscape. Following this, the results of a survey of the visible remains are presented, focusing on the fortifications and two architectural fragments. The site is then discussed within its spatial and chronological context, suggesting that Pergamos was an important settlement in the area, exhibiting traits of a polis, in particular during Archaic and Classical times before it fell within the sphere of influence of the Roman colony Philippi.

Approximate and Exact Optimization Algorithms for the Beltway and Turnpike Problems with Duplicated, Missing, Partially Labeled, and Uncertain Measurements.

The Turnpike problem aims to reconstruct a set of one-dimensional points from their unordered pairwise distances. Turnpike arises in biological applications such as molecular structure determination, genomic sequencing, tandem mass spectrometry, and molecular error-correcting codes. Under noisy observation of the distances, the Turnpike problem is NP-hard and can take exponential time and space to solve when using traditional algorithms. To address this, we reframe the noisy Turnpike problem through the lens of optimization, seeking to simultaneously find the unknown point set and a permutation that maximizes similarity to the input distances. Our core contribution is a suite of algorithms that robustly solve this new objective. This includes a bilevel optimization framework that can efficiently solve Turnpike instances with up to 100,000 points. We show that this framework can be extended to scenarios with domain-specific constraints that include duplicated, missing, and partially labeled distances. Using these, we also extend our algorithms to work for points distributed on a circle (the Beltway problem). For small-scale applications that require global optimality, we formulate an integer linear program (ILP) that (i) accepts an objective from a generic family of convex functions and (ii) uses an extended formulation to reduce the number of binary variables. On synthetic and real partial digest data, our bilevel algorithms achieved state-of-the-art scalability across challenging scenarios with performance that matches or exceeds competing baselines. On small-scale instances, our ILP efficiently recovered ground-truth assignments and produced reconstructions that match or exceed our alternating algorithms. Our implementations are available at https://github.com/Kingsford-Group/turnpikesolvermm.

Deep Reinforcement Learning in Nonstationary Environments With Unknown Change Points.

Deep reinforcement learning (DRL) is a powerful tool for learning from interactions within a stationary environment where state transition and reward distributions remain constant throughout the process. Addressing the practical but challenging nonstationary environments with time-varying state transition or reward function changes during the interactions, ingenious solutions are essential for the stability and robustness of DRL agents. A key assumption to cope with nonstationary environments is that the change points between the previous and the new environments are known beforehand. Unfortunately, this assumption is impractical in many cases, such as outdoor robots and online recommendations. To address this problem, this article presents a robust DRL algorithm for nonstationary environments with unknown change points. The algorithm actively detects change points by monitoring the joint distribution of states and actions. A detection boosted, gradient-constrained optimization method then adapts the training of the current policy with the supporting knowledge of formerly well-trained policies. The previous policies and experience help the current policy adapt rapidly to environmental changes. Experiments show that the proposed method accumulates the highest reward among several alternatives and is the fastest to adapt to new environments. This work has compelling potential for increasing the environmental suitability of intelligent agents, such as drones, autonomous vehicles, and underwater robots.

Residual-based reduced order models for parameterized Navier–Stokes equations with nonhomogeneous boundary condition

In this paper, we propose a residual-based reduced-order model (ROM) framework that utilizes available data to increase the ROM accuracy and stability. The available snapshots are utilized to obtain the original ROM systems and their projection coefficients by performing proper orthogonal decomposition. Then a time-parameter varying closure term is added to the original ROM systems to obtain the new ROM systems, and the values of the closure term at discrete time-parameter points with respect to snapshots are evaluated by computing the residual of the original ROM systems with projection coefficients. In an online testing stage, the values of the closure term at unknown time-parameter points are approximated by linear interpolation, and the new ROM systems are utilized to approximate solutions for unseen parameter values. In addition, the closure term in the new ROM systems is instead discarded when we predict systems' evolution outside the time interval with respect to snapshots. Numerical results show that the proposed method not only improves the prediction accuracy of original ROM but also extends the applicability of it.

Research on Intelligent Ventilation System of Metal Mine Based on Real-Time Sensing Airflow Parameters with a Global Scheme

In ventilation systems of metal mines, the real-time measurement of the airflow field and a reduction in pollutants are necessary for clean environmental management and human health. However, the limited quantitative data and expensive detection technology hinder the accurate assessment of mine ventilation effectiveness and safety status. Therefore, we propose a new method for constructing a mine intelligent ventilation system with a global scheme, which can realize the intelligent prediction of unknown points in the mine ventilation system by measuring the airflow parameters of multiple known points. Firstly, the nodal wind pressure method combined with the Hardy–Cross iterative algorithm is used to solve the mine ventilation network, and the airflow parameters under normal operation and extreme working conditions are simulated, based on which an intelligent ventilation training database is established. Secondly, we compared the airflow parameter prediction ability of three different machine learning models with different neural network models based on the collected small-sample airflow field dataset of a mine roadway. Finally, the depth learning method is optimized to build the intelligent algorithm model of the mine ventilation system, and a large number of three-dimensional simulation data and field measurement data of the mine ventilation system are used to train the model repeatedly to realize the intelligent perception of air flow parameters of a metal mine ventilation network and the construction of an intelligent ventilation system. The results show that the maximum error of a single airflow measurement point is 1.24%, the maximum overall error is 3.25%, and the overall average error is 0.51%. The intelligent algorithm has a good model training effect and high precision and can meet the requirements of the research and application of this project. Through case analysis, this method can predict the airflow parameters of any position underground and realize the real-time control of mine safety.

The shading isophotes: Model and methods for Lambertian planes and a point light

Structure-from-Motion (SfM) and Shape-from-Shading (SfS) are complementary classical approaches to 3D vision. Broadly speaking, SfM exploits geometric primitives from textured surfaces and SfS exploits pixel intensity from the shading image. We propose an approach that exploits virtual geometric primitives extracted from the shading image, namely the level-sets, which we name shading isophotes. Our approach thus combines the strength of geometric reasoning with the rich shading information. We focus on the case of untextured Lambertian planes of unknown albedo lit by an unknown Point Light Source (PLS) of unknown intensity. We derive a comprehensive geometric model showing that the unknown scene parameters are in general all recoverable from a single image of at least two planes. We propose computational methods to detect the isophotes, to reconstruct the scene parameters in closed-form and to refine the results densely using pixel intensity. Our methods thus estimate light source, plane pose and camera pose parameters for untextured planes, which cannot be achieved by the existing approaches. We evaluate our model and methods on synthetic and real images.

Linear-quadratic Tobit regression model with a change point due to a covariate threshold

This paper considers a linear-quadratic Tobit regression model, which is developed for modelling the mixture structure with a line segment and a quadratic segment intersecting at an unknown change point. Due to the smoothness of such model structure, the regression coefficients and the change point can be obtained by directly maximising the likelihood function. The proposed estimator has rapid convergence rate and high estimation accuracy, without other computation burden. The asymptotic properties for the proposed estimator are derived by using empirical process theory. A sup-likelihood ratio test procedure is developed for testing the existence of a change point, and its limiting distributions are derived. The good finite sample performances of the proposed estimator are illustrated by numerical studies and empirical applications to the MGUS and GDP data.

Reconstruction of the Chemical Gas Concentration Distribution Using Partial Convolution-Based Image Inpainting.

An interpolation method, which estimates unknown values with constrained information, is based on mathematical calculations. In this study, we addressed interpolation from an image-based perspective and expanded the use of image inpainting to estimate values at unknown points. When chemical gas is dispersed through a chemical attack or terrorism, it is possible to determine the concentration of the gas at each location by utilizing the deployed sensors. By interpolating the concentrations, we can obtain the contours of gas concentration. Accurately distinguishing the contours of a contaminated region from a map enables the optimal response to minimize damage. However, areas with an insufficient number of sensors have less accurate contours than other areas. In order to achieve more accurate contour data, an image inpainting-based method is proposed to enhance reliability by erasing and reconstructing low-accuracy areas in the contour. Partial convolution is used as the machine learning approach for image-inpainting, with the modified loss function for optimization. In order to train the model, we developed a gas diffusion simulation model and generated a gas concentration contour dataset comprising 100,000 contour images. The results of the model were compared to those of Kriging interpolation, one of the conventional spatial interpolation methods, finally demonstrating 13.21% higher accuracy. This suggests that interpolation from an image-based perspective can achieve higher accuracy than numerical interpolation on well-trained data. The proposed method was validated using gas concentration contour data from the verified gas dispersion modeling software Nuclear Biological Chemical Reporting And Modeling System (NBC_RAMS), which was developed by the Agency for Defense Development, South Korea.

A Nonlinear Hybrid Modeling Method for Pump Turbines by Integrating Delaunay Triangulation Interpolation and an Improved BP Neural Network

In order to solve the problem that the data processing accuracy of the characteristic curves is not high, which affects the accuracy of the simulation of variable-speed pumped storage units, this paper proposes a nonlinear hybrid modeling method for pump turbines by integrating Delaunay triangulation interpolation and an improved back-propagation neural network. Firstly, the improved Sutter transform is used to preprocess the original curve, and the convex hull of the transformed curve is calculated. With the unknown point inside the convex hull, Delaunay triangulation interpolation is used to model the transformed curve. With the unknown point outside the convex hull, the back-propagation neural network is first established based on the input and output, and then the initial weights and thresholds of the network are determined using the mind evolution algorithm to complete the modeling and simulation of the curve. Simulation results show that the proposed method fully integrates the high-precision features of interpolation and the powerful nonlinear fitting ability of the neural network, which greatly improves the efficiency and accuracy of pump turbine modeling.

Continuous-time nonlinear model predictive control based on Pontryagin Minimum Principle and penalty functions

Pontryagin's Minimum Principle (PMP) is a powerful tool for solving Nonlinear Model Predictive Control problems (NMPC), enabling the handling of time-varying input constraints and cost functions. However, applying PMP encounters challenges when state constraints must be satisfied. This arises because the optimal trajectory often requires a blend of unconstrained and constrained arcs with unknown junction points. To address this issue, relaxation methods are frequently explored, where state constraints are replaced with penalty functions. The contributions of this paper are as follows. First, a method of penalty functions allowing for coping with soft state constraints is examined. We prove the recursive feasibility of this method and demonstrate its efficiency in a numerical example. Second, the finite-time practical stability for the optimal reference tracking NMPC problem is addressed. By appropriately choosing the terminal cost, one can guarantee the convergence of the state vector to a predefined neighbourhood of the target state.

Surrogate information transfer and fusion in high-dimensional expensive optimization problems

The Kriging surrogate model is less frequently employed in high-dimensional expensive problems than is the radial basis function (RBF) model. This discrepancy is attributed to the challenge of hyperparameter tuning within the Kriging covariance function, which leads to relatively worse predictive performance. However, the Kriging model still plays a crucial role in providing the predicted uncertainty for new point infill that cannot be replaced by the RBF model. To leverage the advantages of both models, a surrogate information transfer and fusion algorithm is presented. Surrogate information transfer introduces global sensitivity information from the constructed RBF model to the hyperparameter optimization of the Kriging model, reducing the dimensions of hyperparameter tuning and improving its approximation performance. Surrogate information fusion combines the RBF and Kriging models, with the RBF model providing more accurate predictions of unknown points for exploitation, while the Kriging model provides predicted uncertainty for swarm updating and new point infilling to ensure exploration of the solution space. Compared with several state-of-the-art algorithms, the proposed algorithm is evaluated on eight benchmark problems and a in real-world optimization case. The experimental results demonstrate the effectiveness of the surrogate information transfer and fusion methods and the significant superiority of the proposed algorithm over the compared algorithms.

Radioligand therapy in patients with unknown point of origin, other than gastroenteropancreatic or G3 grading neuroendocrine neoplasms

Radioligand therapy in patients with unknown point of origin, other than gastroenteropancreatic or G3 grading neuroendocrine neoplasms

Parallel and energy conservative/dissipative schemes for sine–Gordon and Allen–Cahn equations

The sine–Gordon and Allen–Cahn equations are two typical models in the fields of conservative Hamiltonian systems and dissipative gradient flows, respectively. As the demand for numerical methods that respect intrinsic energy conservation/dissipation laws turns into a fundamental principle, the subsequent computational efficiency is getting more and more desired. Linearly implicit methods, which only require solutions of linear algebraic systems at each time step, have become a popular way to design efficient schemes. However, the overall computational cost of these methods depends heavily on the speed at which the linear system can be solved. In this paper, we propose an alternative approach for developing highly efficient energy-conservative or dissipative schemes for the sine–Gordon and Allen–Cahn equations. Our approach is based on spatial finite difference approximations and is fully implicit at first glance, but actually it is completely decoupled point by point, allowing for the implementation of a scalar nonlinear equation successively with a lower complexity that is comparable only to the degrees of freedom. We further establish the connections between our approach and the classic Itoh–Abe discrete gradient and the partitioned averaged vector field methods, and then propose the generalized Itoh–Abe discrete gradient method, which offers great flexibility in the ordering of updating each unknown point. One immediate benefit is that we can specifically choose an ordering to achieve a naturally parallel computation of the resulting scheme, significantly improving the computational efficiency. Various numerical experiments are presented to illustrate the performance of the proposed schemes.

Local physical gradient-based coupling information interpolate method and application on double-wall turbine blade multidisciplinary analysis

A novel generalization interpolate modification method based on local gradient is presented. The main idea was to remove the sample points located in the gradient direction away from the unknown points compared to the sample points located in the perpendicular direction. This method first uses the Taylor expansion to construct a set of equations and solves the local physical gradient. Subsequently, a scale vector is derived to modify the interpolate space. Three classical interpolation methods were modified: Inverse Distance Weighted (IDW), Radial Basis Function (RBF), and Kriging method. The results of the test functions prove that the proposed method can effectively decrease the interpolate error regardless of low or high dimensional interpolate problem and irrespective of the sample point size. Surface and volume interpolate validations demonstrate that the local gradient generates a large interpolate error, and the modified method can essentially diminish this error with a maximum reduction of 9.94 %. Subsequently, this method is applied to implement a multidisciplinary analysis of a practical double-wall cooling blade with a large physical gradient. The results showed that the pressure and temperature interpolate errors could be decreased by 5.18 % and 14.5 %, respectively, with only an additional 115 s, at the fillet and film hole area where the local gradient was high. When the strength analysis was implemented, the maximum stress error reduction was 25.04 MPa at the stress concentration region.

Psychological and social determinants of adaptation: the impact of finances, loneliness, information access and chronic stress on resilience activation.

Many people who face adversity, such as disasters, demonstrate resilience. However, less is known about reactions to large scale disasters with longer recovery periods. The concern is that protracted disasters may result in more chronic or accumulated stressors with an uncertain or unknown end point and can exhaust the natural coping methods and ability to rebound. Thus, understanding patterns of longer-term disaster recovery, inclusive of resilience, is needed. Further resilience is not individual specific rather social determinants, such as support networks and available resources, are contributing factors. The purpose of this study is to improve understanding of mental health and resilience during increased stress, we aim to identify profiles of adaptation and psychological and social determinants that predict membership within predominant symptom groupings. We conducted an exploratory cross-section study (N = 334) with two phases of multivariate analysis. Latent profile models were estimated to identify groups based on depression, anxiety, and resilience scores. The second phase included a step-wise multinomial logistic regression to predict class membership. We identified four distinct groups: 33% of participants were categorized as anxious, 18% depressed, 9% comorbid, and 40% with above average levels of resilience. Psychosocial factors such as demographics, trauma history, information access, loneliness, and lack of financial resources predicted poorer mental health outcomes and lower resilience. This study identified factors that contribute to overall wellbeing despite chronic stressors. Social determinants of adaptation, found in this study population, include loneliness, finances, and information access. The findings from this study support the need for both psychological and social adaption supports, inclusive of mental health treatment, to strengthen resilience activation.

Wildlife fecal microbiota exhibit community stability across a longitudinal semi-controlled non-invasive sampling experiment

Wildlife microbiome studies are being used to assess microbial links with animal health and habitat. The gold standard of sampling microbiomes directly from captured animals is ideal for limiting potential abiotic influences on microbiome composition, yet fails to leverage the many benefits of non-invasive sampling. Application of microbiome-based monitoring for rare, endangered, or elusive species creates a need to non-invasively collect scat samples shed into the environment. Since controlling sample age is not always possible, the potential influence of time-associated abiotic factors was assessed. To accomplish this, we analyzed partial 16S rRNA genes of fecal metagenomic DNA sampled non-invasively from Rocky Mountain elk (Cervus canadensis) near Yellowstone National Park. We sampled pellet piles from four different elk, then aged them in a natural forest plot for 1, 3, 7, and 14 days, with triplicate samples at each time point (i.e., a blocked, repeat measures (longitudinal) study design). We compared fecal microbiota of each elk through time with point estimates of diversity, bootstrapped hierarchical clustering of samples, and a version of ANOVA–simultaneous components analysis (ASCA) with PCA (LiMM-PCA) to assess the variance contributions of time, individual and sample replication. Our results showed community stability through days 0, 1, 3 and 7, with a modest but detectable change in abundance in only 2 genera (Bacteroides and Sporobacter) at day 14. The total variance explained by time in our LiMM-PCA model across the entire 2-week period was not statistically significant (p>0.195) and the overall effect size was small (<10% variance) compared to the variance explained by the individual animal (p<0.0005; 21% var.). We conclude that non-invasive sampling of elk scat collected within one week during winter/early spring provides a reliable approach to characterize fecal microbiota composition in a 16S rDNA survey and that sampled individuals can be directly compared across unknown time points with minimal bias. Further, point estimates of microbiota diversity were not mechanistically affected by sample age. Our assessment of samples using bootstrap hierarchical clustering produced clustering by animal (branches) but not by sample age (nodes). These results support greater use of non-invasive microbiome sampling to assess ecological patterns in animal systems.

A Bagging-SVM field-road trajectory classification model based on feature enhancement

Field-road trajectory classification, in which the semantic labels of unknown trajectory points are predicted by learning the features of agricultural machinery trajectories, has recently received considerable attention in agriculture. At present, most agricultural machinery trajectory samples have imbalanced data distribution problems, and existing field-road trajectory classification methods, which usually use a small set of trajectory features, cannot fully mine the potential information of trajectories, resulting in a low classification accuracy. To address these problems, this paper proposes a Bagging-SVM field-road trajectory classification model based on feature enhancement. First, we use oversampling and undersampling methods to obtain balanced agricultural machinery trajectory data. Second, local features and global features are defined to perform in-depth mining of the spatiotemporal information. Specifically, the local features are derived by calculating the attributes of the dataset, and then the global features are calculated from the local features using the statistical magnitude. Next, principal component analysis (PCA) is employed to identify significant features and decrease the dimensionality. Finally, we introduce the bagging integrated learning method in support vector machines to construct Bagging-SVM as a classifier for field-road trajectory classification. To verify the effectiveness of the proposed method, we conduct experiments using 20 trajectory samples with high-frequency sampling frequencies and 20 trajectory samples with low-frequency sampling frequencies, and the classification F1 score of this method reach 97.01% and 98.71% on two trajectory dataset, respectively. The experimental results show that our method outperforms existing field-road trajectory classification methods.

Fast approximation algorithms for some maximin clustering problems

In this paper, we consider three cases of an intractable problem of searching for two subsets in a finite set of points of Euclidean space. In all three cases, it is required to maximize the minimum cluster?s cardinality under constraint on each cluster?s scatter. The scatter is the sum of the distances from the cluster elements to the center, which is defined differently in each of the three cases. In the first case, cluster centers are fixed points. In the second case, the centers are unknown points from the input set. In the third case, the centers are defined as the centroids (the arithmetic mean) of clusters. We propose a general scheme that allows us to build a polynomial 1/2-approximation algorithm for a generalized problem and can be used for constructing 1/2-approximation algorithms for the first two cases and for the one-dimensional third case. Also we show how, using precomputed general information, their time complexities can be reduced to the complexity of sorting. Finally, we present the results of computational experiments showing the accuracy of the proposed algorithms on randomly generated input data.

HETEROSKEDASTİK VERİLERDE BİLİNMEYEN DEĞİŞİM NOKTALARININ TESPİT EDİLMESİ

There are several tests to detect structural change at unknown change points. The Andrews Sup F test (1993) is the most powerful, but it requires the assumption of homoskedasticity. Ahmed et al. (2017) introduced the Sup MZ test, which relaxes this assumption and tests for changes in both the coefficients of regression and variance simultaneously. In this study, we propose a model update procedure that uses the Sup MZ test to detect structural changes at unknown change points. We apply this procedure to model the weekly returns of the Istanbul Stock Exchange's common stock index (BIST 100) for a 21-year period (2003-2023). Our model consists simply a mean plus noise, with occasional jumps in the level of mean or variance at unknown times. The goal is to detect these jumps and update the model accordingly. We also suggest a trading rule that uses the forecasts from our procedure and compare it to the buy-and-hold strategy.