In this article, we investigate the problem of parameter identification of spatial–temporal varying processes described by a general nonlinear partial differential equation and validate the feasibility and robustness of the proposed algorithm using a group of coordinated mobile robots equipped with sensors in a realistic diffusion field. Based on the online parameter identification method developed in our previous work using multiple mobile robots, in this article, we first develop a parameterized model that represents the nonlinear spatially distributed field, then develop a parameter identification scheme consisting of a cooperative Kalman filter and recursive least square method. In the experiments, we focus on the diffusion field and consider the realistic scenarios that the diffusion field contains obstacles and hazard zones that the robots should avoid. The identified parameters together with the located source could potentially assist in the reconstruction and monitoring of the field. To validate the proposed methods, we generate a controllable carbon dioxide (CO2) field in our laboratory and build a static CO2 sensor network to measure and calibrate the field. With the reconstructed realistic diffusion field measured by the sensor network, a multi-robot system is developed to perform the parameter identification in the field. The results of simulations and experiments show satisfactory performance and robustness of the proposed algorithms.

The particularity of nuclear power plant environment requires that the nuclear power inspection robot must be remote control operation. The main purpose of the inspection robot is to carry out inspection, prevention, reporting, and safety emergency operation on the instruments, so as to provide guarantee for the safe operation of the nuclear power plant. Based on the representative configuration of nuclear power robot at home and abroad, this paper develops a small and lightweight nuclear power plant inspection robot, including walking mechanism, lifting mechanism, operating mechanism, image acquisition, information communication and control system, etc., to carry on the statics analysis to the key components of the inspection robot and verify that the stiffness and strength of the mechanical structure meet the requirements of lightweight design. Modal analysis is carried out to verify that the motor does not cause resonance when working. The kinematic model of the robot has been established and can provide the theoretical basis for the controller design. A hierarchical control system based on LabVIEW upper computer monitoring and control operation interface is established, which uses adaptive fuzzy Proportional Integral Derivative (PID) control to simulate the walking control, and then realizes the control of walking mechanism through software programming, and the adaptive fuzzy PID control has better effect than the conventional PID control. The S-type acceleration and deceleration algorithm is used to realize the accurate control of the position location of the lifting mechanism. Finally, combined with the experiment of 5MS robot comprehensive experimental platform, it is proved that the inspection robot can realize remote control function operation.

In chemical process engineering, surrogate models of complex systems are often necessary for tasks of domain exploration, sensitivity analysis of the design parameters, and optimization. A suite of computational fluid dynamics (CFD) simulations geared toward chemical process equipment modeling has been developed and validated with experimental results from the literature. Various regression-based active learning strategies are explored with these CFD simulators in-the-loop under the constraints of a limited function evaluation budget. Specifically, five different sampling strategies and five regression techniques are compared, considering a set of four test cases of industrial significance and varying complexity. Gaussian process regression was observed to have a consistently good performance for these applications. The present quantitative study outlines the pros and cons of the different available techniques and highlights the best practices for their adoption. The test cases and tools are available with an open-source license to ensure reproducibility and engage the wider research community in contributing to both the CFD models and developing and benchmarking new improved algorithms tailored to this field.

Personalized PageRank has found many uses in not only the ranking of webpages, but also algorithmic design, due to its ability to capture certain geometric properties of networks. In this paper, we study the diffusion of PageRank: how varying the jumping (or teleportation) constant affects PageRank values. To this end, we prove a gradient estimate for PageRank, akin to the Li–Yau inequality for positive solutions to the heat equation (for manifolds, with later versions adapted to graphs).

Real numbers do not admit an extensional procedure for observing discrete information, such as the first digit of its decimal expansion, because every extensional, computable map from the reals to the integers is constant, as is well known. We overcome this by considering real numbers equipped with additional structure, which we call a locator. With this structure, it is possible, for instance, to construct a signed-digit representation or a Cauchy sequence, and conversely, these intensional representations give rise to a locator. Although the constructions are reminiscent of computable analysis, instead of working with a notion of computability, we simply work constructively to extract observable information, and instead of working with representations, we consider a certain locatedness structure on real numbers.

We consider a dynamic network cascade process developed by Duncan Watts applied to a class of random networks, developed independently by Newman and Miller, which allows a specified amount of clustering (short loops). We adapt existing methods for locally tree-like networks to formulate an appropriate two-type branching process to describe the spread of a cascade started with a single active node and obtain a fixed-point equation to implicitly express the extinction probability of such a cascade. In so doing, we also recover a formula that has appeared in various forms in works by Hackett et al. and Miller which provides a threshold condition for certain extinction of the cascade. We find that clustering impedes cascade propagation for networks of low average degree by reducing the connectivity of the network, but for networks with high average degree, the presence of small cycles makes cascades more likely.

Superbubbles are acyclic induced subgraphs of a digraph with single entrance and exit that naturally arise in the context of genome assembly and the analysis of genome alignments in computational biology. These structures can be computed in linear time and are confined to non-symmetric digraphs. We demonstrate empirically that graph parameters derived from superbubbles provide a convenient means of distinguishing different classes of real-world graphical models, while being largely unrelated to simple, commonly used parameters.

This paper addresses the issue of monitoring spatial environmental phenomena of interest utilizing information collected by a network of mobile, wireless, and noisy sensors that can take discrete measurements as they navigate through the environment. It is proposed to employ Gaussian Markov random field (GMRF) represented on an irregular discrete lattice by using the stochastic partial differential equations method to model the physical spatial field. It then derives a GMRF-based approach to effectively predict the field at unmeasured locations, given available observations, in both centralized and distributed manners. Furthermore, a novel but efficient optimality criterion is then proposed to design centralized and distributed adaptive sampling strategies for the mobile robotic sensors to find the most informative sampling paths in taking future measurements. By taking advantage of conditional independence property in the GMRF, the adaptive sampling optimization problem is proven to be resolved in a deterministic time. The effectiveness of the proposed approach is compared and demonstrated using pre-published data sets with appealing results.

Distribution of wireless power charging field uniformly on a large area pad is critical for power receivers, particularly for wearable devices, wherein small form factor coils are involved. Since the receiver coil size is quite limited in these types of applications, the device is very sensitive to the amount of field it could retain and hence, it needs special placement or snapping mechanism to fix it at an optimum location for reliable wireless charging. In order to overcome this limitation for the end-user, a dual-mode multi-coil power transceiver system is proposed; utilizing resonance filtering to increase the amount of total power delivered with the rather uniform spatial distribution. Two concentric coils; center one driven by 6.78-MHz high-frequency driver (A4WP) and the outer larger one with a 200-KHz low-frequency driver (Qi) with resonant blocker could transfer up to 50 mW standards compliant flat power to a 13-mm radius 30-turns wearable receiver coil everywhere across an 8-cm radius charging pad area without any alignment requirement or snapping. Two different feedback topologies corresponding to each of the H-Bridge power drivers were also presented as an automatic series resonance coil drive frequency lock mechanism, extracting peak powers for each system individually from a standard 5 V-1A USB wall charger.

We prove a ‘resilience’ version of Dirac’s theorem in the setting of random regular graphs. More precisely, we show that whenever d is sufficiently large compared to $\epsilon > 0$, a.a.s. the following holds. Let $G'$ be any subgraph of the random n-vertex d-regular graph $G_{n,d}$ with minimum degree at least $$(1/2 + \epsilon )d$$. Then $G'$ is Hamiltonian.

This proves a conjecture of Ben-Shimon, Krivelevich and Sudakov. Our result is best possible: firstly the condition that d is large cannot be omitted, and secondly the minimum degree bound cannot be improved.

Let M be an n × m matrix of independent Rademacher (±1) random variables. It is well known that if $n \leq m$, then M is of full rank with high probability. We show that this property is resilient to adversarial changes to M. More precisely, if $m \ge n + {n^{1 - \varepsilon /6}}$, then even after changing the sign of (1 – ε)m/2 entries, M is still of full rank with high probability. Note that this is asymptotically best possible as one can easily make any two rows proportional with at most m/2 changes. Moreover, this theorem gives an asymptotic solution to a slightly weakened version of a conjecture made by Van Vu in [17].

The New pandemic is hitting all around the world in different manner. The infection rate, prevalence and severity is not patronized in many countries. Pakistan is now attaining the peak in its cases. Around 108,317 confirmed cases are present in Pakistan and 71,127 are currently active cases1. The recovery rate is 32%. The dangerous situation about the infection prevalence is that most of the people either are asymptomatic of having mild symptoms. An estimation release from Primary Health Department of Punjab claims that almost 670,000 cases are asymptomatic in only Lahore city 2. This correspondence is about an infected family of 5 people 3 males and 2 females in Lahore, Pakistan. Who were exposed with virus and one after one got the infection, the two males deceased but rest of family member are recovered.

Leishmania species are the causative agents for Leishmaniasis which is one of the neglected tropical diseases causing 70,000 deaths worldwide each year. Squalene synthase enzyme plays a vital role in sterol metabolism which is essential for Leishmania parasite viability. Therefore squalene synthase of Leishmania donovani is a therapeutic target to inhibit growth of parasite. The 3D model of Leishmania donovani Squalene Synthase (LdSQS) was generated by homology modeling and validated through PROCHECK, ERRAT, VERIFY3D and PROSA tools. Virtual screening of the protein was performed by AutoDock with reported inhibitor, E5700 and two natural alkaloids. Molecular interactions were explored to understand the nature of intermolecular bonds between active ligand and the protein binding site residues using UCSF Chimera and PLIP server. The reported inhibitor showed the best binding affinity (-9.75 kcal/mol) closely followed by Ancistrotanzanine B (-9.55 kcal/mol) and Holamine (-8.79 kcal/mol). Ancistrotanzanine B showed low binding energy and permissible ADMET properties. Based on the present study, homology model of LdSQS and Ancistrotanzanine B can be used to design inhibitors with antileishmanial activity.

An improved FastSLAM based on the robust square-root cubature Kalman filter (RSRCKF) with partial genetic resampling is proposed in this paper. In the proposed method, RSRCKF is used to design the proposal distribution of FastSLAM and to estimate environment landmarks. The proposed method does not require a priori knowledge of the noise statistics. In addition, to increase diversity, it uses the genetic operators-based strategy to further improve the particle diversity. In fact, a partial genetic resampling operation is carried out to maintain the diversity of particles. The proposed method is compared with other methods via simulation and experimental data. It can be seen from the results that the proposed method provides significantly more accurate and robust estimation results compared with other methods even with fewer particles and unknown a priori. In addition, the consistency of the proposed method is better than that of other methods.

Many types of interactive applications, including reactive systems implemented in hardware, interactive physics simulations and games, raise particular challenges when it comes to testing and debugging. Reasons include de facto lack of reproducibility and difficulties of automatically generating suitable test data. This paper demonstrates that certain variants of functional reactive programming (FRP) implemented in pure functional languages can mitigate such difficulties by offering referential transparency at the level of whole programs. This opens up for a multi-pronged approach for assisting with testing and debugging that works across platforms, including assertions based on temporal logic, recording and replaying of runs (also from deployed code), and automated random testing using QuickCheck. When combined with extensible forms of FRP that allow for constrained side effects, it allows us to not only validate software simulations but to analyse the effect of faults in reactive systems, confirm the efficacy of fault tolerance mechanisms and perform software- and hardware-in-the-loop testing. The approach has been validated on non-trivial systems implemented in several existing FRP implementations, by means of careful debugging using a tool that allows the test or simulation under scrutiny to be controlled, moving along the execution time line, and pin-pointing of violations of assertions on personal computers as well as external devices.

Is a logicist bound to the claim that as a matter of analytic truth there is an actual infinity of objects? If Hume’s Principle is analytic then in the standard setting the answer appears to be yes. Hodes’s work pointed to a way out by offering a modal picture in which only a potential infinity was posited. However, this project was abandoned due to apparent failures of cross-world predication. We re-explore this idea and discover that in the setting of the potential infinite one can interpret first-order Peano arithmetic, but not second-order Peano arithmetic. We conclude that in order for the logicist to weaken the metaphysically loaded claim of necessary actual infinities, they must also weaken the mathematics they recover.

In this paper, we discuss stochastic orderings of lifetimes of two heterogeneous parallel and series systems with heterogeneous dependent components having generalized Birnbaum–Saunders distributions. The comparisons presented here are based on the vector majorization of parameters. The ordering results are established in some special cases for the generalized Birnbaum–Saunders distribution based on the multivariate elliptical, normal, t, logistic, and skew-normal kernels. Further, we use these results by considering Archimedean copulas to model the dependence structure among systems with generalized Birnbaum–Saunders components. These results have been used to derive some upper and lower bounds for survival functions of lifetimes of parallel and series systems.