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In the autonomous ocean monitoring task, the sampling robot moves in the environment and accumulates data continuously. The widely adopted spatial modeling method - standard Gaussian process (GP) regression - becomes inadequate in processing the growing sensing data of a large size. To overcome the computational challenge, this paper presents an environmental modeling framework using a sparse variant of GP called streaming sparse GP (SSGP). The SSGP is able to handle streaming data in an online and incremental manner, and is therefore suitable for long-term autonomous environmental monitoring. The SSGP summarizes the collected data using a small set of pseudo data points that best represent the whole dataset, and updates the hyperparameters and pseudo point locations in a streaming fashion, leading to high-quality approximation of the underlying environmental model with significantly reduced computational cost and memory demand., Proceedings of OCEANS 2019, SEATTLE

With the fast development of offshore wind power, the fluctuation of its output has an increasingly significant impact on the secure and stable operation of power system. Aiming at the large-scale offshore wind farms (OWFs) integrated, a method for selecting optimal access point is proposed. The capability of wind power accommodation, voltage stability, transmission network vulnerability and construction cost are considered as evaluation indexes. Based on the theory of information entropy and fuzzy analytic hierarchy process, taking the minimum deviation of the comprehensive weight and maximum of the compressive weight value as the optimization goal, the combination weighting model is established to determine the index weight. Simulation result of Shandong Power grid of China demonstrate the effectiveness of the proposed method for selecting the access point of the OWF.

In this paper, an optimized rectangular corrugated tube is used for the super-critical water reactor (SCWR) cooling, numerically. Al2O3, CuO, Fe3O4 and TiO2 nanoparticles in different shapes (spherical, brick, platelet and cylindrical) are considered as additives to SCW for cooling purposes, and it is tried to introduce the most suitable cases from the maximum heat transfer view point. Piecewise temperature-dependent properties are considered for the SCW and different correlations such as Brinkman and Pak equations were examined for nanofluid properties. The governing equation are solved based on control volume method (CVM) software, where the SCW properties are introduced to it by a user defined function (UDF). As the main outcome, Pak and Cho correlation had the best fitted curves with other presented functions. Also, the corrugated tube filled by spherical alumina-SCW had the best performance for SCWR cooling.

This paper is concerned with the recovery of (approximate) solutions to parabolic problems from incomplete and possibly inconsistent observational data, given on a time-space cylinder that is a strict subset of the computational domain under consideration. Unlike previous approaches to this and related problems our starting point is a regularized least squares formulation in a continuous infinite-dimensional setting that is based on stable variational time-space formulations of the parabolic partial differential equation. This allows us to derive a priori as well as a posteriori error bounds for the recovered states with respect to a certain reference solution. In these bounds the regularization parameter is disentangled from the underlying discretization. An important ingredient for the derivation of a posteriori bounds is the construction of suitable Fortin operators which allow us to control oscillation errors stemming from the discretization of dual norms. Moreover, the variational framework allows us to contrive preconditioners for the discrete problems whose application can be performed in linear time, and for which the condition numbers of the preconditioned systems are uniformly proportional to that of the regularized continuous problem. In particular, we provide suitable stopping criteria for the iterative solvers based on the a posteriori error bounds. The presented numerical experiments quantify the theoretical findings and demonstrate the performance of the numerical scheme in relation with the underlying discretization and regularization.

In this study, we provide a characterization of the class of proportional bargaining solutions introduced by Kalai. Our result differs from earlier axiomatizations in that we use a property that we label individual disagreement point concavity. This property is a weakening of disagreement point concavity used by Chun and Thomson. An application illustrates the potential usefulness of our new property in a strategic setting.

In this analysis, the cubic B-spline method is employed for constructing the approximate solutions of a class of fractional two-point boundary value problems. These fractional problems are expressed in terms of the conformable fractional derivative approach. More deeply, a class of conformable Lane-Emden model is considering and modifying with singularities. Many numerical applications are presented and discussed to exhibit the feasibility and efficiency of the procedure involved in both linear and non-linear cases. The numerical findings are quite similar to those of the exact solutions as well as require relatively less computational work. Latterly, remarks and future research work are provided.

In this paper, we study a boundary value problem consisting of Hahn integro-difference equation supplemented with four-point fractional Hahn integral boundary conditions. The novelty of this problem lies in the fact that it contains two fractional Hahn difference operators and three fractional Hahn integrals with different quantum numbers and orders. Firstly, we convert the given nonlinear problem into a fixed point problem, by considering a linear variant of the problem at hand. Once the fixed point operator is available, we make use the classical Banach's and Schauder's fixed point theorems to establish existence and uniqueness results. An example is also constructed to illustrate the main results. Several properties of fractional Hahn integral that will be used in our study are also discussed.

In this work, we consider a coupled nonlinear viscoelastic Kirchhoff equations with degenerate damping, dispersion and source terms. Under suitable hypothesis, we will prove that when the initial data are large enough (in the energy point of view), the energy grows exponentially and thus so the $ L^{2(p+2)} $-norm.

Due to associated uncertainties, modelling the spatial distribution of depth to bedrock (DTB) is an important and challenging concern in many geo-engineering applications. The association between DTB, the safety and economy of design structures implies that generating more precise predictive models can be of vital interest. In the present study, the challenge of applying an optimally predictive three-dimensional (3D) spatial DTB model for an area in Stockholm, Sweden was addressed using an automated intelligent computing design procedure. The process was developed and programmed in both C++ and Python to track their performance in specified tasks and also to cover a wide variety of different internal characteristics and libraries. In comparison to the ordinary Kriging (OK) geostatistical tool, the superiority of the developed automated intelligence system was demonstrated through the analysis of confusion matrices and the ranked accuracies of different statistical errors. The results showed that in the absence of measured data, the intelligence models as a flexible and efficient alternative approach can account for associated uncertainties, thus creating more accurate spatial 3D models and providing an appropriate prediction at any point in the subsurface of the study area.

The reliability of using abrupt changes in the spatial Hurst exponent for identifying temporal points of abrupt change in climate dynamics is explored. If a spatio-temporal dynamical system undergoes an abrupt change at a particular time, the time series of spatial Hurst exponent obtained from the data of any variable of the system should also show an abrupt change at that time. As expected, spatial Hurst exponents for each of the two variables of a model spatio-temporal system – a globally coupled map lattice based on the Burgers' chaotic map – showed abrupt change at the same time that a parameter of the system was changed. This method was applied for the identification of change points in climate dynamics using the NCEP/NCAR data on air temperature, pressure and relative humidity variables. Different abrupt change points in spatial Hurst exponents were detected for the data of these different variables. That suggests, for a dynamical system, change point detected using the two-dimensional detrended fluctuation analysis method on a single variable alone is insufficient to comment about the abrupt change in the system dynamics and should be based on multiple variables of the dynamical system. HIGHLIGHTS In a non-linear dynamical system, the spatio-temporal images of one variable also contain information about the interaction of this variable with other coupled variables of the system.; When a parameter of the dynamical system changes abruptly, the spatial distribution of all the coupled variables should show an abrupt change simultaneously.; It is found that the 2D-DFA method successfully detected abrupt change points in each variable of GCML based on Burgers' chaotic map at the same time as a system parameter was changed.; The 2D-DFA method detected different abrupt change points in the time series of the spatial Hurst for the NCEP/NCAR data of three different variables.; This study suggests, for a dynamical system, change point detected using 2D-DFA method on a single variable alone is insufficient to comment about the abrupt change in the system dynamics and should be based on multiple variables of the dynamical system.

In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably. The suction cup is used for lifting the object from the clutter first and the gripper for grasping the object accordingly. We utilize the affordance map to provide pixel-wise lifting point candidates for the suction cup. To obtain a good affordance map, the active exploration mechanism is introduced to the system. An effective metric is designed to calculate the reward for the current affordance map, and a deep Q-Network (DQN) is employed to guide the robotic hand to actively explore the environment until the generated affordance map is suitable for grasping. Experimental results have demonstrated that the proposed robotic grasping system is able to greatly increase the success rate of the robotic grasping in cluttered scenes., Comment: has been accepted by IEEE/RSJ International Conference on Intelligent Robots and Systems 2019

The Big Match is a multistage two-player game. In each stage, player 1 hides one or two pebbles in his hand, and his opponent has to guess that number. Player 1 loses a point if player 2 is correct; otherwise, he wins a point. As soon as player 1 hides one pebble, the players cannot change their choices in any future stage. The undiscounted Big Match has been much-studied. Blackwell and Ferguson (1968) give an [Formula: see text]-optimal strategy for player 1 that hides, in each stage, one pebble with a probability that depends on the entire past history. Any strategy that depends on just the clock or just a finite memory is worthless (i.e., cannot guarantee strictly more than the least reward). The long-standing natural open problem has been whether every strategy that depends on just the clock and a finite memory is worthless. The present paper proves that there is such a strategy that is [Formula: see text]-optimal. In fact, we show that just two states of memory are sufficient.

Change point detection plays a fundamental role in many real-world applications, where the goal is to analyze and monitor the behaviour of a data stream. In this paper, we study change detection in binary streams. To this end, we use a likelihood ratio between two models as a measure for indicating change. The first model is a single bernoulli variable while the second model divides the stored data in two segments, and models each segment with its own bernoulli variable. Finding the optimal split can be done in \( \mathcal {O} \mathopen {}\left( n\right) \) time, where n is the number of entries since the last change point. This is too expensive for large n. To combat this we propose an approximation scheme that yields \((1 - \epsilon )\) approximation in \( \mathcal {O} \mathopen {}\left( \epsilon ^{-1} \log ^2 n\right) \) time. The speed-up consists of several steps: First we reduce the number of possible candidates by adopting a known result from segmentation problems. We then show that for fixed bernoulli parameters we can find the optimal change point in logarithmic time. Finally, we show how to construct a candidate list of size \( \mathcal {O} \mathopen {}\left( \epsilon ^{-1} \log n\right) \) for model parameters. We demonstrate empirically the approximation quality and the running time of our algorithm, showing that we can gain a significant speed-up with a minimal average loss in optimality.

This paper presents a set of adaptive reset techniques for use with haptic retargeting systems focusing on interaction with hybrid virtual reality interfaces that align with a physical interface. Haptic retargeting between changing physical and virtual targets requires a reset where the physical and virtual hand positions are re-aligned. We present a modified Point technique to guide the user in the direction of their next interaction such that the remaining distance to the target is minimized upon completion of the reset. This, along with techniques drawn from existing work are further modified to consider the angular and translational gain of each redirection and identify the optimal position for the reset to take place. When the angular and translational gain is within an acceptable range, the reset can be entirely omitted. This enables continuous retargeting between targets removing interruptions from a sequence of retargeted interactions. These techniques were evaluated in a user study which showed that adaptive reset techniques can provide a significant decrease in task completion time, travel distance, and the number of user errors Refereed/Peer-reviewed

Forecast reconciliation is a post-forecasting process aimed to improve the quality of the base forecasts for a system of hierarchical/grouped time series (Hyndman et al., 2011). Contemporaneous (cross-sectional) and temporal hierarchies have been considered in the literature, but - except for Kourentzes and Athanasopoulos (2019) - generally these two features have not been fully considered together. Adopting a notation able to simultaneously deal with both forecast reconciliation dimensions, the paper shows two new results: (i) an iterative cross-temporal forecast reconciliation procedure which extends, and overcomes some weaknesses of, the two-step procedure by Kourentzes and Athanasopoulos (2019), and (ii) the closed-form expression of the optimal (in least squares sense) point forecasts which fulfill both contemporaneous and temporal constraints. The feasibility of the proposed procedures, along with first evaluations of their performance as compared to the most performing `single dimension' (either cross-sectional or temporal) forecast reconciliation procedures, is studied through a forecasting experiment on the 95 quarterly time series of the Australian GDP from Income and Expenditure sides considered by Athanasopoulos et al. (2019)., Main text: 49 pages, 10 figures, 2 tables. Appendix: 68 pages, 29 figures, 17 tables

This paper examined the impact of a network attack on a congested transmission session. The research is motivated by the fact that the previous research community has neglected to evaluate security issues related to network congestion environments, and has instead concentrated on resolving congestion issues only. At any point in time, attackers can take advantage of the congestion problem, exploit the attack surface, and inject attack vectors. In order to circumvent this issue, a machine learning algorithm is trained to correlate attack vectors from the attack surface in a network congestion signals environment with the value of decisions over time in order to maximise expected attack vectors from the attack surface. Experimental scenario that dwell on transmission rate overwhelming transmission session, resulting in a standing queue was used. The experiment produced a dataset in which a TCP transmission through bursting transmission were capture. The data was acquired using a variety of experimental scenarios. Nave Bayes, and K-Nearest Neighbours prediction analyses demonstrate strong prediction performance. As a result, this study re-establishes the association between attack surface and vectors with network attack prediction.

Since 2017, many deep learning methods for 3-D point clouds observed by airborne LiDAR (airborne 3-D point clouds) have been proposed. Moreover, not only a deep learning method for airborne 3-D point clouds but also a deep learning method for points and their waveforms observed by full-waveform LiDAR (airborne FW data) was proposed. We need to achieve highly accurate land cover classification by using airborne FW data, but open data often only have airborne 3-D point clouds available. Therefore, to improve the performance of land cover classification when using airborne 3-D point clouds published as open data, it is important to restore waveforms from airborne 3-D point clouds. In this article, we propose a deep learning model to translate an airborne 3-D point cloud to airborne FW data (called a point-to-waveform translation model, point2wave) using a conditional generative adversarial net (cGAN). Our point2wave is a cGAN pipeline consisting of a generator that translates the waveform corresponding to each point from the input airborne 3-D point cloud and discriminators that calculate the distance between the translated waveform and the ground truth waveform. Using a set of point clouds and waveforms dataset, we have experimented to translate points into the waveforms by point2wave. Experimental results showed that point2wave could translate waveforms from the airborne 3-D point cloud and the translated fake waveforms achieved nearly the same land cover classification performance as the real waveforms.

[EN]A hedgehog or Bloch point is a point-like 3D magnetization configuration in a ferromagnet. Regardless of widely spread treatment of a Bloch point as a topological defect, its 3D topological charge has never been calculated. Here, applying the concepts of the emergent magnetic field and Dirac string, we calculate the 3D topological charge (Hopf index) of a Bloch point and show that due to the magnetostatic energy contribution it has a finite, non-integer value. Thus, Bloch points form a new class of hopfions-3D topological magnetization configurations. The calculated Bloch point non-zero gyrovector leads to important dynamical consequences such as the appearance of topological Hall effect. F.T. acknowledges support by ANID-PFCHA/Postdoctorado Becas Chile 74200122. K.G. acknowledges support by IKERBASQUE (the Basque Foundation for Science). This work was supported by the Spanish Ministry of Science and Innovation under grants FIS2016-78591-C3-3-R, PID2019-108075RB-C31, PID2019-108075RB-C33 and PID2019-106802GB-I00 /AEI/1013039/501100011033.

Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy (STEM) at high speed, with the potential for vast volumes of data to be acquired in relatively short times or through autonomous experiments that can continue over very long periods. Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way. However, like many other tasks related to object detection and identification in artificial intelligence, it is challenging to detect and identify defects from STEM images. Furthermore, it is difficult to deal with crystal structures that have many atoms and low symmetries. Previous methods used for defect detection and classification were based on supervised learning, which requires human-labeled data. In this work, we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine (OCSVM). We introduce two schemes of image segmentation and data preprocessing, both of which involve taking the Patterson function of each segment as inputs. We demonstrate that this method can be applied to various defects, such as point and line defects in 2D materials and twin boundaries in 3D nanocrystals.

In this paper, we suggested and analyzed a new higher-order iterative algorithm for solving nonlinear equation $$g(x)=0$$ g ( x ) = 0 , $$g:{\mathbb {R}}\longrightarrow {\mathbb {R}}$$ g : R ⟶ R , which is free from derivative by using the approximate version of the first derivative, and we studied the basins of attraction for the proposed iterative algorithm to find complex roots of complex functions $$g:{\mathbb {C}}\longrightarrow {\mathbb {C}}$$ g : C ⟶ C . To show the effectiveness of the proposed algorithm for the real and the complex domains, the numerical results for the considered examples are given and graphically clarified. The basins of attraction of the existing methods and our algorithm are offered and compared to clarify their performance. The proposed algorithm satisfied the condition such that $$|x_{m}-\alpha | | x m - α | < 1.0 × 10 - 15 , as well as the maximum number of iterations is less than or equal to 3, so the proposed algorithm can be applied to efficiently solve numerous type non-linear equations.

Inrush of limestone karst water through the mine floor occurs frequently in the Carboniferous–Permian coalfield in northern China. The unascertained measurement evaluation and set pair analysis methods are proposed to evaluate water inflow, by using eight indices. Firstly, ordered binary comparison quantization method and interval number fuzzy decision matrix of EA-TOPSIS ordering were used to obtain weights for these eight factors. Secondly, conflicting evidence theory is applied to couple of these weights calculated by ordered binary comparison quantization and EA-TOPSIS ordering, to ensure the effective evaluation of the relative importance with respect to each indicator for the dynamic model. Lastly, the unapprehend measurement evaluation and set pair analysis methods were utilized to evaluate the risk of limestone karst water influx from coal seam floor. Based on the type of index value, the state classification is carried out through quantitative analysis, and single index measure functions including linear and non-linear patterns, are constructed. The process is described as follows: first, the 8 index values collected from 30 water inrush points in Xinwen coalfield are substituted into the measurement function to construct the multi index comprehensive evaluation matrix. Second, the credibility identification criterion is introduced to determine the risk level of water incurred point X i Finally, the general situation of water inflow risk from coal seam floor during coal mining in Xinwen coalfield is illustrated by using set pair potential analysis method. The production practice in Xinwen coalfield has proved that the evaluation results are consistent with the actual situation.

This paper describes the condition analysis of a multicopter carried with a proposed device for rough terrain landing. Based on a multicopter carried with an electrical robot arm for grasping, we proposed a method to determine whether the skid-carried multicopter can land on an arbitrary slope or not. We established the static model of the entire device, and analyzed the conditions under which the arm and skid can contact the arbitrary plane and the COG (Center of Gravity), which includes the mass of passive skid, multicopter body and each link of the robot arm. Further, we proposed a method to analyze whether the entire device can land stably. By analyzing that the projection of the entire device’s COG is inside or outside the triangle, that comprises the contact point between the device and the uneven ground, we can determine whether the device can land successfully and the condition for capable landing is concluded. After the numerical analysis, the verification experiment is conducted, and by comparing the result of analysis with the experiment, the accuracy of the analysis can be demonstrated.

This paper conducts a comparative study of proximal gradient methods (PGMs) and proximal DC algorithms (PDCAs) for sparse regression problems which can be cast as Difference-of-two-Convex-functions (DC) optimization problems. It has been shown that for DC optimization problems, both General Iterative Shrinkage and Thresholding algorithm (GIST), a modified version of PGM, and PDCA converge to critical points. Recently some enhanced versions of PDCAs are shown to converge to d-stationary points, which are stronger necessary condition for local optimality than critical points. In this paper we claim that without any modification, PGMs converge to a d-stationary point not only to DC problems but also to more general nonsmooth nonconvex problems under some technical assumptions. While the convergence to d-stationary points is known for the case where the step size is small enough, the finding of this paper is valid also for extended versions such as GIST and its alternating optimization version, which is to be developed in this paper. Numerical results show that among several algorithms in the two categories, modified versions of PGM perform best among those not only in solution quality but also in computation time., Theorem 5.2. and its proof; We have added an assumption and modified the proof because the previous version (and Theorem 2 of the published version in Optimization Letters) had some mathematical errors. Furthermore, we have corrected some typos

application/pdf, Nonlinear Dynamics. 2021, 106 (3), P.2363-2376

Objective To study the value of multitime point salivary pepsin testing (MTPSPT) for the diagnosis of laryngopharyngeal reflux (LPR). Study Design Prospective noncontrolled. Methods For patients who met the enrollment criteria, the reflux symptom index (RSI) and reflux finding score (RFS) were calculated and salivary pepsin testing was performed. The pepsin test was performed every hour from 7:00 a.m. to 6:00 p.m. by collecting fresh saliva samples. A single positive test result was needed for the diagnosis of LPR. The consistency in the diagnosis of LPR between the two methods was compared with the weighted Cohen's kappa statistic. Results A total of 204 patients were included. The kappa value between the two methods was 0.566 (p = .00). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MTPSPT were 76.43%, 85.94%, 92.24%, and 62.5%, respectively. We also compared a single pepsin measure at 7 a.m. with the screening results based on the RSI and RFS, and found a much lower kappa agreement value (0.223, p = .00). The sensitivity, specificity, PPV, NPV, and false‐negative rate of pepsin testing at 7 a.m. (fasting) were 37.86%, 92.18%, 91.38%, 40.41%, and 58.57%, respectively. Conclusion The use of the result of a single salivary pepsin test in the morning yields a relatively higher rate of missed diagnosis of LPR, and multitime point testing through a day increased the accuracy and sensitivity of detection of LPR twofold compared to a single morning fasting sample. Level of Evidence 3

We consider a family of variational problems on the equilibrium of a composite Kirchhoff–Love plate containing two flat rectilinear rigid inclusions, which are connected in a hinged manner. It is assumed that both inclusions are delaminated from an elastic matrix, thus forming an interfacial crack between the inclusions and the surrounding elastic media. Displacement boundary conditions of an inequality type are set on the crack faces that ensure a mutual nonpenetration of opposite crack faces. The problems of the family depend on a parameter specifying the coordinate of a connection point of the inclusions. For the considered family of problems, we formulate a new inverse problem of finding unknown coordinates of a hinge joint point. The continuity of solutions of the problems on this parameter is proved. The solvability of this inverse problem has been established. Using a passage to the limit, a qualitative connection between the problems for plates with flat and bulk hinged inclusions is shown.

Dissimilarity representation plays a very important role in pattern recognition due to its ability to capture structural and relational information between samples. Dissimilarity space embedding is an approach in which each sample is represented as a vector based on its dissimilarity to some other samples called prototypes. However, lack of neighborhood-preserving, fixed and usually considerable prototype set for all training samples cause low classification accuracy and high computational complexity. To address these challenges, our proposed method creates dissimilarity space considering the neighbors of each data point on the manifold. For this purpose, Locally Linear Embedding (LLE) is used as an unsupervised manifold learning algorithm. The only goal of this step is to learn the global structure and the neighborhood of data on the manifold and mapping or dimension reduction is not performed. In order to create the dissimilarity space, each sample is compared only with its prototype set including its k-nearest neighbors on the manifold using the geodesic distance metric. Geodesic distance metric is used for the structure preserving and is computed using the weighted LLE neighborhood graph. Finally, Latent Space Model (LSM), is applied to reduce the dimensions of the Euclidean latent space so that the second challenge is resolved. To evaluate the resulted representation ad so called dissimilarity space, two common classifiers namely K Nearest Neighbor (KNN) and Support Vector Machine (SVM) are applied. Experiments on different datasets which included both Euclidean and non-Euclidean spaces, demonstrate that using the proposed approach, classifiers outperform the other basic dissimilarity spaces in both accuracy and runtime.

The primary focus of the presented research is to come up with a model that could be utilized to evaluate the permeate content (concentration) of oil drops using a straight (nonconverging) slotted microstructured membrane. The content (concentration) of crude drops in the permeate with a nonconverging slit structure membrane has not been studied before, and the study presented would be a good contribution to the literature. A comparison between the use of a converging (narrowing toward the inside) and a nonconverging slotted pore microstructured membrane is made for the purpose of removing oil content from the produced water. Due to the drag force, the droplets pass through the membrane slots; however, the static force acts in the opposite direction and tries to reject droplets by the membrane. At a certain point, these two forces balance the effect of each other, which is known as "100% cutoff through the membrane". A linear line is obtained by joining the 100% cutoff or rejection point to the 0% rejection point, which is referred to as the "linear fit" in this paper. The linear fit approach could be utilized for estimating rejection below the 100% cutoff point. Various types of crude oil drops obtained from different locations were analyzed experimentally, and the results were compared with the presented model. The proposed model was found to be in agreement with the different types of oil drops. Experimental and predicted results showed that the nonconverging slotted microstructured membrane provided low friction to oil drops through the membrane as compared to the converging slots. Furthermore, the developed model can be utilized to predict the overall oil content in the permeate. This research has great importance and will allow researchers around the globe to estimate crude oil concentration within the allowable limits.

In this paper, we introduce new concepts, admissible multivalued hybrid $\mathcal{Z}$-contractions and multivalued hybrid $\mathcal{Z}$-contractions in the framework of $b$-metric spaces and establish sufficient conditions for existence of fixed points for such contractions. A few consequences of our main theorem involving linear and nonlinear contractions are pointed out and discussed by using variants of simulation functions. In the case where our notions are reduced to their single-valued counterparts, the results presented herein complement, unify and generalize a number of significant fixed point theorems due to Branciari, Czerwik, Jachymski, Karapinar and Argawal, Khojasteh, Rhoades, among others. Nontrivial illustrative examples are provided to support the assertions of the obtained results. From application point of view, some fixed point theorems of $b$-metric spaces endowed with partial ordering and graph are deduced and solvability conditions of nonlinear matrix equations are investigated.

Smart meters implemented at the end-user in the energy sector create the opportunity to develop data-intelligent methods for district heating systems by using a large amount of fine-granular heat consumption time series from end-users. The current state-of-the-art methods for temperature control in district heating systems rely on predefined critical points in the network and a set reference temperature curve that expresses the minimum forward temperature as a function of the outdoor temperature at the end-users. The critical points are used to ensure that the consumers’ supply temperature requirements are met all times. To predefine the critical points at the network, the location of the lowest temperature in the grid needs to be identified at any point in time. Since the lowest temperature often varies over time, one must have a set of critical points in a district heating network. This paper proposes a method to estimate the temperature at an artificial critical point for the network using time-wise quantile estimation using smart meter data at end-users. This novel approach eliminates the need for physical critical points in the net or sensors in wells and creates the possibility of changing the critical point location if needed. The benefits for the provider of using smart meters as feedback, makes the measurement wells redundant and flexibility of the location. The location of low temperature areas in the network can change overtime hence the flexibility of being able to change where the feedback is located. The proposed method to replace the well measurements to provide feedback for temperature control at the production site groups a predefined set of smart meter readings together for each point in time. The grouping is done to have reliable measurements from each smart meter device, excluding some of the meters when a faulty reading occurs. The set of acceptable readings is used to estimate the street pipe’s temperature using the estimated quantile of the forward temperature. The approach is tested on two demo cases. The first demo consists of smart meters to estimate the forward temperature of the main street pipe. The second demo uses three smart meters at large apartment buildings as feedback for the control. Initial results show that the estimated temperature of the network can replace the well-measurements which traditionally are used as feedback for temperature control and give a better and more flexible control.

Deep neural networks (DNNs) and convolutional neural networks (CNNs) have demonstrated greater robustness and accuracy in classifying two-dimensional images and three-dimensional point clouds compared to more traditional machine learning approaches. However, their main drawback is the need for large quantities of semantically labeled training data sets, which are often out of reach for those with resource constraints. In this study, we evaluated the use of simulated 3D point clouds for training a CNN learning algorithm to segment and classify 3D point clouds of real-world urban environments. The simulation involved collecting light detection and ranging (LiDAR) data using a simulated 16 channel laser scanner within the the CARLA (Car Learning to Act) autonomous vehicle gaming environment. We used this labeled data to train the Kernel Point Convolution (KPConv) and KPConv Segmentation Network for Point Clouds (KP-FCNN), which we tested on real-world LiDAR data from the NPM3D benchmark data set. Our results showed that high accuracy can be achieved using data collected in a simulator.

In wireless sensor networks, dynamic environmental changes may lead to the distribution of some sensors becoming very dense, which leads to the waste of communication resources in distributed estimation. In this paper, it is proposed that Event‐triggered distributed dynamic adaptive‐neighbour selection LMS (ET‐dyANDLMS) algorithm can effectively solve the problems mentioned above. On the one hand, the network communication resources are reduced by the adaptive‐neighbour selection. On the other hand, the network data redundancy is reduced by the event‐trigger mechanism. From a theoretical point of view, the algorithm converges stably in mean meaning and mean square meaning, and the communication cost is reduced compared with other distributed algorithms. The theoretical analysis results verified by experimental simulation demonstrate the effectiveness of the proposed algorithm.

This research investigates the effect of downstream drain’s geometry on seepage through homogeneous earth dams founded on an impervious foundation. A permeability tank experimental model and SEEP2D numerical model are used in the study. The dam’s failure in the case of no drain is experimentally observed to point out the drain’s importance. The effect of the drain’s geometry on seepage characteristics is evaluated by considering different scenarios for its height, length, and angle. Three cases for reservoir filling are studied. Sensitivity analysis is performed to evaluate the drain’s most effective geometrical parameter. The results show that the most effective geometrical parameter of the downstream drain is its length, while its height and angle have no effect. Increasing the drain’s length increases the seepage discharge and the distance between the phreatic line and downstream face and reduces the pore water pressure leading to more safety. Design charts and equations are provided.

In this work, we solve the system of integro-differential equations (in terms of Caputo–Fabrizio calculus) using the concepts of the best proximity pair (point) and measure of noncompactness. We first introduce the concept of cyclic (noncyclic) Θ-condensing operator for a pair of sets using the measure of noncompactness and then establish results on the best proximity pair (point) on Banach spaces and strictly Banach spaces. In addition, we have illustrated the considered system of integro-differential equations by three examples and discussed the stability, efficiency, and accuracy of solutions.

As per most other earth science engineering problems, the underground coal geotechnical environment and the way in which roof and rib support interacts with the rock mass are complex issues. It is therefore generally recognised that without prudent simplification, the complexity of the problem will overwhelm all current geotechnical methods of modelling, not least for the reason that a rock mass can never be characterised to a level that allows a “non-simplified” analysis. The fact that numerical models, which are commonly purported to be a “simulation” tool and the so-called epitome of advanced geotechnical engineering, always need to be “calibrated” to a known reality is taken to be conclusive proof of this statement. While the problem should not be oversimplified (i.e. the dominant failure mechanisms or critical data input parameters should not be ignored), without question judicious simplification is at the heart of all engineering design, to the point that it has a well-established name – “reductionism”. The hypothesis addressed in this paper, is that horizontal and vertical stress-driven slender beam and column behaviour (which includes unstable Euler Buckling) are respectively the dominant (but not only) roadway roof and ribline behavioural mechanism that (if not controlled) can lead to excessive deformation, failure and eventual collapse. As a part of the Scientific Method, a hypothesis can only be tested via real-world observations, measurements and analyses in establishing it is a credible Theory. Utilising the Scientific Method, this paper demonstrates that slender beam/column behaviour is the dominant instability mechanism within a coal mine roof/rib subject to elevated horizontal/vertical stress conditions and therefore, must be representatively accounted for in any credible empirical, analytical, or numerical approach to coal mine roof/rib stability assessment and associated ground support design.

It is theoretically and numerically shown that when tightly focusing an n-th order vector light field that has the central V-point (at which the linear polarization direction is undetermined), the polarization singularity index n, and a "flower"-shaped intensity pattern with 2(n-1) lobes it forms a transverse intensity distribution with 2(n-1) local maxima. At the same time, a vector light field with the polarization singularity index -n, which has the form of a "web" with 2(n+1) cells generates at the sharp focus a transverse intensity distribution with 2(n+1) local maxima. In the focal spot, either 2(n-1) or 2(n+1) V-point polarization singularities with alternating indices +1 or -1 are formed at the intensity zero.

This paper proposes a method for estimating the effective zone, including effective depth and effective range of compaction degree, from rapid impact compaction (RIC) on sand layer whose fines content is less than 10%. The proposed method utilizes a string of microelectromechanical system accelerometers to monitor the acceleration at various depths and propagation distances during compaction. To interpret and extract useful information from monitored data, peak-over-threshold (POT) processing and normal distribution function were used to analyze the recorded acceleration. The mean and standard deviation of the threshold peak acceleration were used to evaluate the effective depth and the effective range of compaction degree during RIC compaction. Moreover, spatial contours were used to determine the correlation of the threshold peak acceleration against depth and propagation distance from the RIC impact point. These contours help indicating the distribution of the effect zone after compaction. Lastly, a proposed method is suggested for frequent use in trial tests to quickly determine RIC’s required depth and impact spacing.

In this paper an efficient model based diagnostic process is described for systems whose components possess a causal relation between their inputs and their outputs. In this diagnostic process, firstly, a set of focuses on likely broken components is determined. Secondly, for each focus the most informative probing point within the focus can be determined. Both these steps of the diagnostic process have a worst case time complexity of ${\cal O}(n^2)$ where $n$ is the number of components. If the connectivity of the components is low, however, the diagnostic process shows a linear time complexity. It is also shown how the diagnostic process described can be applied in dynamic systems and systems containing loops. When diagnosing dynamic systems it is possible to choose between detecting intermitting faults or to improve the diagnostic precision by assuming non-intermittency.

International audience; UDO is a versatile tool for offline tuning of database systems for specific workloads. UDO can consider a variety of tuning choices, reaching from picking transaction code variants over index selections up to database system parameter tuning. UDO uses reinforcement learning to converge to near-optimal configurations, creating and evaluating different configurations via actual query executions (instead of relying on simplifying cost models). To cater to different parameter types, UDO distinguishes heavy parameters (which are expensive to change, e.g. physical design parameters) from light parameters. Specifically for optimizing heavy parameters, UDO uses reinforcement learning algorithms that allow delaying the point at which the reward feedback becomes available. This gives us the freedom to optimize the point in time and the order in which different configurations are created and evaluated (by benchmarking a workload sample). UDO uses a cost-based planner to minimize reconfiguration overheads. For instance, it aims to amortize the creation of expensive data structures by consecutively evaluating configurations using them. We evaluate UDO on Postgres as well as MySQL and on TPC-H as well as TPC-C, optimizing a variety of light and heavy parameters concurrently.

Distance measure is one of the research hotspot in Pythagorean fuzzy environment due to its quantitative ability of distinguishing Pythagorean fuzzy sets (PFSs). Various distance functions for PFSs are introduced in the literature and have their own pros and cons. The common thread of incompetency for these existing distance functions is their inability to distinguish highly uncertain PFSs distinctly. To tackle this point, we introduce a novel distance measure for PFSs. An added advantage of the measure is its simple mathematical form. Moreover, superiority and reasonability of the prescribed definition are demonstrated through proper numerical examples. Boundedness and nonlinear behaviour of the distance measure is established and verified via suitable illustrations. In the current scenario, selecting an antivirus face-mask as a preventive measure in the COVID-19 pandemic and choosing the best school in private sector for children are some of the burning issues of a modern society. These issues are addressed here as multi-attribute decision-making problems and feasible solutions are obtained using the introduced definition. Applicability of the distance measure is further extended in the areas of pattern recognition and medical diagnosis.

In this paper, existence and uniqueness results are established for a nonlinear sequential Hadamard fractional differential equation with multi-point boundary conditions, via Banach and Krasnosel'skiĭ's fixed point theorems and Leray-Schauder nonlinear alternative. An example illustrating the existence of a unique solution is also constructed.

A divide-and-conquer (DAC) machine learning approach was first proposed by Wang et al. to forecast the sea surface height (SSH) of the Loop Current System (LCS) in the Gulf of Mexico. In this DAC approach, the forecast domain was divided into non-overlapping partitions, each of which had their own prediction model. The full domain SSH prediction was recovered by interpolating the SSH across each partition boundaries. Although the original DAC model was able to predict the LCS evolution and eddy shedding more than two months and three months in advance, respectively, growing errors at the partition boundaries negatively affected the model forecasting skills. In the study herein, a new partitioning method, which consists of overlapping partitions is presented. The region of interest is divided into 50%-overlapping partitions. At each prediction step, the SSH value at each point is computed from overlapping partitions, which significantly reduces the occurrence of unrealistic SSH features at partition boundaries. This new approach led to a significant improvement of the overall model performance both in terms of features prediction such as the location of the LC eddy SSH contours but also in terms of event prediction, such as the LC ring separation. We observed an approximate 12% decrease in error over a 10-week prediction, and also show that this method can approximate the location and shedding of eddy Cameron better than the original DAC method.

With the introduction of effective and general deep learning network frameworks, deep learning based methods have achieved remarkable success in various visual tasks. However, there are still tough challenges in applying them to convolutional neural networks due to the lack of a potential rule structure of point clouds. Therefore, by taking the original point clouds as the input data, this paper proposes an orientation-encoding (OE) convolutional module and designs a convolutional neural network for effectively extracting local geometric features of point sets. By searching for the same number of points in 8 directions and arranging them in order in 8 directions, the OE convolution is then carried out according to the number of points in the direction, which realizes the effective feature learning of the local structure of the point sets. Further experiments on diverse datasets show that the proposed method has competitive performance on classification and segmentation tasks of point sets.

In this paper we propose a solution to the nonlinear Fredholm integral equations in the context of w-distance. For this purpose, we also provide a fixed point result in the same setting. In addition, we provide best proximity point results. We give examples and present numerical results to approximate fixed points.

A method integrating terrestrial laser scanning (TLS) and finite element modelling (FEM) is proposed in this study. It aims at assessing the structural deformation of a historic brick-masonry building, the Beamless Hall at Linggu Temple in Nanjing, China. The building was composed of a series of vaults and arches, the largest among whom spans over 11m. TLS (Z+F Imager5010X) was used to collect 3D point cloud with high density. Point slices and geometric feature computation (verticality) were employed to detect geometric displacement quantitatively and intuitively. FEM-simulation was based on an ideal 3D model ignoring geometric anomalies. Results show that the Beamless Hall has inherent structural defect owing to its asymmetric layout along the transverse axis. Computing geometric feature of point cloud is fast and intuitive to detect and show geometric deviation. Inferred by FEM-simulated results and TLS-based deviation analysis, the building’s asymmetrical layout under self-weight is probably the main reason causing its structural deformation. Further developments include FEM based on as-built geometry, corrected materials parameters, and a comprehensive geometric deviation analysis.

Hydrotalcites are an important class of layered materials, displaying ion-exchange, adsorption, and base catalytic properties. The crystallization kinetics for hydrotalcites are however hardly available. Nevertheless, as their reconstruction from the oxides (also called as "memory effect") is considered a synthesis route, this can be used to study the crystallization phenomena. This note looks at the reconstruction kinetics of a Mg-Al hydrotalcite using previously reported kinetic expressions. It was found that high temperature is beneficial if the process is controlled by nucleation. The temperature effect is less obvious when the process reaches a diffusional control regime. For example, temperature is beneficial to shorten the processing time in a nucleation-regime conversion (e.g., 40%). However, to achieve a high conversion (e.g., 98-99%), an intermediate temperature shows the optimal condition, i.e., shortest processing time. The work shows useful side effects of diffusional control. It also highlights the importance of obtaining the kinetics over the entire range for process optimization and, finally, emphasizes that both parameters in the Avrami-Erofe'ev model impact the time required to achieve a given conversion. Directions for further studies to understand the kinetic-process relationships have been highlighted.

We give another proof of Toyoda's theorem that describes 5-point subpaces in CAT(0) length spaces, Comment: 9 pages, 8 figures

We associate the stationary harmonic oscillator with time-dependent systems exhibiting non-Hermiticity by means of point transformations. The new systems are exactly solvable, with all-real spectra, and transit to the Hermitian configuration for the appropriate values of the involved parameters. We provide a concrete generalization of the Swanson oscillator that includes the Caldirola–Kanai model as a particular case. Explicit solutions are given in both the classical and quantum pictures.

Scientists were introduced to the Golgi apparatus (GA) in 1898, when it was discovered by Camillo Golgi in 1898 as a -cytoplasmic reticular network-. Researchers heard Camillo Golgi-s name not only because of the GA, but also because of many definitions such as Golgi silver impregnation techniques, Golgi type I and II cells, Golgi cells of the cerebellum, and Golgi tendon organ. In fact, although the GA beared the name of this scientist, many scientists did numerous studies on the morphological and functional properties of this unique organelle before him, simultaneously with him or after him. Despite the simple technical possibilities of the old times, the scientists, whom we gratefully commemorated, obtained magnificent findings about the GA and presented them to the world of science. In this short article, which was a review, the following historical developments, starting from the discovery of the GA, were summarized.