Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Measurement of Dolphin Thrust Generated during a Tail Stand Using DPIV

Estimation of force generated by dolphins has long been debated. The problem was that indirect estimates of force production for dolphins resulted in low values that could not be validated. Bubble digital particle image velocimetry (DPIV) measured hydrodynamic force production for swimming dolphins and demonstrated high force production. To validate the bubble DPIV and reconcile force production measurements, two bottlenose dolphins (Tursiops truncatus) performing tail stands were measured with bubble DPIV. Microbubbles were generated from a finely porous hose and compressed air source. Displacement of the bubbles by the propulsive motions of the dolphin was tracked with a high-speed video camera. Oscillations of the dolphin flukes generated strong vortices and a downward directed jet flow into the wake. Application of the Kutta–Joukowski theorem measuring vortex circulations yielded forces up to 997.3 N. Another video camera recorded body height above the water surface to determine the mass-force of the dolphin above the water surface. For the dolphin to hold its position above the water surface, the mass-force approximately balanced the vertical hydrodynamic force from the flukes. The results demonstrated the fluke motions generate high sustained forces roughly equal to the dolphin’s weight out of the water. Bubble DPIV validated high forces measured previously for thrust generated in swimming by animals and demonstrated a more accurate technique compared to standard aerodynamic analysis.
Rigid 3-dimensional compacta whose squares are manifolds

A space is rigid if its only self-homeomorphism is the identity. In response to a question of Jan van Mill, Ancel and Singh have given examples of rigid «-dimensional compacta, for each n > 4, whose squares are manifolds. We construct a rigid 3-dimensional compactum whose square is the manifold S3 X S3. In fact, we construct uncountably many topologically distinct compacta with these properties.
De-biasing low-rank projection for matrix completion

We study matrix completion with non-uniform, deterministic sampling patterns. We introduce a computable parameter, which is a function of the sampling pattern, and show that if this parameter is small, then we may recover missing entries of the matrix, with appropriate weights. We theoretically analyze a simple and well-known recovery method, which simply projects the (zero-padded) subsampled matrix onto the set of low-rank matrices. We show that under non-uniform deterministic sampling, this method yields a biased solution, and we propose an algorithm to de-bias it. Numerical simulations demonstrate that de-biasing significantly improves the estimate. However, when the observations are noisy, the error of this method can be sub-optimal when the sampling is highly non-uniform. To remedy this, we suggest an alternative which is based on projection onto the max-norm ball whose robustness to noise tolerates arbitrarily non-uniform sampling. Finally, we analyze convex optimization in this framework.
Exploring cyclists’ travel behaviour in different attitudinal market segments: case study of Quelimane, Mozambique

ABSTRACT Distinguishing bicycle market segments is relevant to promote bicycle use, as customised policy strategies can be targeted to each submarket. This paper aims to adopt the attitudinal market segmentation approach to identify potential markets of bicycle commuters and their travel behaviour differences. A survey was conducted among 902 commuters in Quelimane, Mozambique. Factor analysis is used to identify key latent factors. Two-step cluster analysis is used to segment the bicycle commuting market into several submarkets. Then, Mann-Whitney U test is used to compare pairs of segments based on a set of travel behaviour factors such as cycling frequency, travel time and commuting mode. Three segments are obtained: Reluctant cyclists (S1), Livelihood cyclists (S2), and Demanding cyclists (S3). The study reveals that S1 cycle less frequently when compared to S2 and S3. S1 can be motivated to cycle more frequently by promoting educational campaigns to increase their perception towards cycling benefits. S1 and S3 commute long hours when compared to S2, thus, promoting distribution of land-use activities to reduce travel distance could motivate bicycle use. S1 frequently walk commute compared to S2 and S3, therefore policies to increase bicycle use were pointed to making bicycles more accessible to Reluctant cyclists.
Stimulated zonal flow generation in the case of TEM and TIM microturbulence

In this paper, we show that in some parameter range in gyrokinetic simulations, it is possible to apply a control method to stimulate the appearance of zonal flows while minimizing the duration of the control process and the impact on plasma parameters. For this purpose, a gyrokinetic code considering only trapped particles is used. The starting point of our work is a situation where zonal flows transiently appear after the nonlinear phase of saturation of trapped electron modes or trapped ion modes' micro-instabilities. These are observed to be strongly reduced in a later phase, permitting streamers to govern the plasma behavior in the steady-state. By intervening during this latter state (after this transient growth and decay of zonal flow), i.e., by increasing the ion/electron temperature ratio for a short time, it is found to be possible to bifurcate to a new steady-state, in which zonal flows are strongly present and are maintained indefinitely, thereby allowing a significant reduction in radial heat.
Practically Finite-Time Control for Nonlinear Systems With Mismatching Conditions and Application to a Robot System

This paper is concerned with the practically finite-time (PFT) control problem for a class of more general nonlinear systems subject to mismatching time-varying disturbances. Without any extra assumptions on system nonlinearities, a composite controller is developed by introducing a disturbance observer and finite-time control technique. Based on the designed controller and Lyapunov stability theory, the PFT stability of the closed-loop system is strictly verified and proven to be a better convergence performance. Furthermore, as a byproduct of the proposed design method, the disturbance observer-based PFT control for high-order nonlinear systems is also shown to be possible. To the best of the authors’ knowledge, it is the first PFT control result for high-order nonlinear systems with external disturbances. Finally, an application example of a single-link robot system with disturbances and a numerical example are presented to demonstrate the effectiveness of the proposed scheme, respectively.
Active disturbance rejection control to MIMO nonlinear systems with stochastic uncertainties: approximate decoupling and output-feedback stabilisation

ABSTRACT In this paper, we apply the active disturbance rejection control, an emerging control technology, to output-feedback stabilisation for a class of uncertain multi-input multi-output nonlinear systems with vast stochastic uncertainties. Two types of extended state observers (ESO) are designed to estimate both unmeasured states and stochastic total disturbance which includes unknown system dynamics, unknown stochastic inverse dynamics, external stochastic disturbance without requiring the statistical characteristics, uncertain nonlinear interactions between subsystems, and uncertainties caused by the deviation of control parameters from their nominal values. The estimations decouple approximately the system after cancelling stochastic total disturbance in the feedback loop. As a result, we are able to design an ESO-based stabilising output-feedback and prove the practical mean square stability for the closed-loop system with constant gain ESO and the asymptotic mean square stability with time-varying gain ESO, respectively. Some numerical simulations are presented to demonstrate the effectiveness of the proposed output-feedback control scheme.
Genetic diversity of Ethiopian barley (Hordeum vulgare L.) genotypes based on multivariate statistical analysis for acid soil tolerance

Soil acidity is now a serious threat to barley production in most high lands of Ethiopia. Three hundred twenty (320) barley genotypes were evaluated in 2017 main-season, at Holeta Agricultural Research Center using 20x16 Alpha Lattice design under two-soil conditions (limed and unlimed). The objectives of the study were to evaluate the genetic variability among barley genotypes for soil acidity tolerance using multivariate analysis. Barley genotypes were classified into thirteen, sixteen and twelve clusters under unlimed and limed soil condition and by stress index cluster analysis, respectively. Principal component analysis exhibited 81% and 78% of total variation under unlimed and limed soil condition respectively. Phenotypic diversity index was very high for ear attitude, kernel row number and Kernel color and comparatively low for spike density.
Coexistence of the spini-density-wave and superconductivity in the Ba1-xKxFe2As2

The relation between the spin-density-wave (SDW) and superconducting order is a central topic in current research on the FeAs-based high T{sub c} superconductors. Conflicting results exist in the LaFeAs(O,F)-class of materials, for which whether the SDW and superconductivity are mutually exclusive or they can coexist has not been settled. Here we show that for the (Ba,K)Fe{sub 2}As{sub 2} system, the SDW and superconductivity can coexist in an extended range of compositions. The availability of single crystalline samples and high value of the energy gaps would make the materials a model system to investigate the high T{sub c} ferropnictide superconductivity.
A Kernel Probabilistic Model for Semi-supervised Co-clustering Ensemble

Abstract Co-clustering is used to analyze the row and column clusters of a dataset, and it is widely used in recommendation systems. In general, different co-clustering models often obtain very different results for a dataset because each algorithm has its own optimization criteria. It is an alternative way to combine different co-clustering results to produce a final one for improving the quality of co-clustering. In this paper, a semi-supervised co-clustering ensemble is illustrated in detail based on semi-supervised learning and ensemble learning. A semi-supervised co-clustering ensemble is a framework for combining multiple base co-clusterings and the side information of a dataset to obtain a stable and robust consensus co-clustering. First, the objective function of the semi-supervised co-clustering ensemble is formulated according to normalized mutual information. Then, a kernel probabilistic model for semi-supervised co-clustering ensemble (KPMSCE) is presented and the inference of KPMSCE is illustrated in detail. Furthermore, the corresponding algorithm is designed. Moreover, different algorithms and the proposed algorithm are used for experiments on real datasets. The experimental results demonstrate that the proposed algorithm can significantly outperform the compared algorithms in terms of several indices.
A method of transition conflict resolving in hierarchical control

The paper concerns the problem of automatic solving of transition conflicts in hierarchical concurrent state machines (also known as UML state machine). Preparing by the designer a formal specification of a behaviour free from conflicts can be very complex. In this paper, it is proposed a method for solving conflicts through transition predicates modification. Partially specified predicates in the nondeterministic diagram are transformed into a symbolic Boolean space, whose points of the space code all possible valuations of transition predicates. Next, all valuations under partial specifications are logically multiplied by a function which represents all possible orthogonal predicate valuations. The result of this operation contains all possible collections of predicates, which under given partial specification make that the original diagram is conflict free and deterministic.
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions

Modern applications in engineering and data science are increasinglybased on multidimensional data of exceedingly high volume, variety,and structural richness. However, standard machine learning algorithmstypically scale exponentially with data volume and complexityof cross-modal couplings - the so called curse of dimensionality -which is prohibitive to the analysis of large-scale, multi-modal andmulti-relational datasets. Given that such data are often efficientlyrepresented as multiway arrays or tensors, it is therefore timely andvaluable for the multidisciplinary machine learning and data analyticcommunities to review low-rank tensor decompositions and tensor networksas emerging tools for dimensionality reduction and large scaleoptimization problems. Our particular emphasis is on elucidating that,by virtue of the underlying low-rank approximations, tensor networkshave the ability to alleviate the curse of dimensionality in a numberof applied areas. In Part 1 of this monograph we provide innovativesolutions to low-rank tensor network decompositions and easy to interpretgraphical representations of the mathematical operations ontensor networks. Such a conceptual insight allows for seamless migrationof ideas from the flat-view matrices to tensor network operationsand vice versa, and provides a platform for further developments, practicalapplications, and non-Euclidean extensions. It also permits theintroduction of various tensor network operations without an explicitnotion of mathematical expressions, which may be beneficial for manyresearch communities that do not directly rely on multilinear algebra.Our focus is on the Tucker and tensor train TT decompositions andtheir extensions, and on demonstrating the ability of tensor networksto provide linearly or even super-linearly e.g., logarithmically scalablesolutions, as illustrated in detail in Part 2 of this monograph.
Smart analysis of anxiety people and their activities using heterogeneous quasiperiodic process

The increase in anxiety levels worldwide can be described as a serious global health threat. Around 500 million people suffer from mental disorders and are suffering from depression, and other mental‐oriented disabilities. The new technological paradigms such as the Internet of Things (IoT) were employed for detecting, and treating these disorders, which are being proposed, developed, and provide new capabilities to detect, assess and care for anxious people. These possibilities lead to several issues that are identified, which relate to patient privacy and confidentiality, security challenges such as data security, and the organization of IoT systems. To rectify these issues, we implement the Smart analysis of anxious people and their activities using Heterogeneous computing with virtual sensing. This system consists of the health application featuring the technique of the internet of things, heterogeneous computing, Cognitive Quasiperiodic motion, and Pentagonal tiling. This system introduces the internet of things‐assisted smart analysis of anxiety against people with their activities. It performs the optimization analysis for improving the levels. The observational results focus on how to deal with the issues which were overcome in the analysis levels of the anxiety disorders, which show that time spent using the heterogeneous computing with the virtual sense proves more accuracy (92.3%), specificity (Degrees of anxiety severity by 20%), precision (Analysis of anxiety stress level by 65%), and recall (Anxiety chances percentile by 65.34%). The proposed model is simulated, and the outcomes are compared with the prevailing methods for evaluating the parameters like accuracy, end‐end‐end delay, energy consumption, network lifetime, and throughput.
Liquid jet breakup unsteadiness in a coaxial air-blast atomizer

The aim of this paper is to experimentally characterize the liquid jet breakup unsteadiness in a coaxial air-blast atomizer. The current research focuses on the measurement of the fluctuations of the jet breakup length and the flapping instability of the liquid jet, which contribute to the downstream fluctuations of the spray characteristics. The optical connectivity technique was used to measure the instantaneous breakup length of the water jet. Also, time resolved shadowgraph images of the primary jet breakup process were captured by high-speed imaging to characterize the jet instabilities at different axial locations from the atomizer exit. Experiments were performed for a wide range of air-to-liquid momentum flux ratio (M) and aerodynamic Weber number (Weg) corresponding to membrane- and/or fiber breakup mode of the jet disintegration process. The mean jet breakup length was found to vary inversely with M through a power law relation in agreement with the literature, while the breakup length fluctuations were found to first decrease and then increase with M. In order to capture the unsteady dynamics of the jet breakup process, the proper orthogonal decomposition analysis of the optical connectivity images was performed. The jet flapping and the fluctuations of the jet breakup length were identified as the second and the third spatial proper orthogonal decomposition modes, respectively, for all operating conditions of the atomizer. The amplitude and the frequency of the instabilities were measured by temporal tracking of the liquid–air interface on the shadowgraph images. The disturbance close to the injector exit corresponds to the Kelvin–Helmholtz instability, while close to the jet breakup point the jet exhibits the flapping instability, which is characterized by lateral oscillation of the jet about the atomizer axis. The influence of the liquid jet Reynolds number and momentum flux ratio on the KH and the flapping instabilities are examined.
Regional stability of two‐dimensional nonlinear polynomial Fornasini‐Marchesini systems

This paper addresses the problem of regional stability analysis of 2‐dimensional nonlinear polynomial systems represented by the Fornasini‐Marchesini second state‐space model. A method based on a polynomial Lyapunov function is proposed to ensure local asymptotic stability and provide an estimate of the domain of attraction of the system zero equilibrium point. The proposed results that build on recursive algebraic representations of the polynomial vector function of the system dynamics and Lyapunov function are tailored via linear matrix inequalities that are required to be satisfied at the vertices of a given bounded convex polyhedral region of the state space. Numerical examples demonstrate the effectiveness of the proposed method.
An Integrated Pump-Controlled Variable Coupler Fabricated by Ultrafast Laser Writing

The design and fabrication of a integrated symmetric directional coupler dependent o the pumping power and operating at a 1534 nm wavelength is reported. The twin-core waveguide was inscribed into Er3+/Yb3+ co-doped phosphate glass by a femtosecond laser direct writing technique. By optical pumping, the coupling ratio can be modulated due to the changes induced in the refractive index of the material. The experimental results demonstrated that the coupling ratio can be tuned continuously from 100/0 to 50/50 by increasing the pump’s power from 0 to 350 mW. The developed twin-core coupler has promising applications for on-chip all-optical signal processing and communication systems.
Delay_dependent H∞ stochastic stability analysis for stochastic nonlinear delay hybrid singular system with time delay

In this paper, the problem of H∞ nonlinear stochastic stability analysis for a class singular systems with Markovian jumping parameters and time-varying delay is addressed. The discrete delay is assumed to be time-varying and belong to a given interval. Based on LMIs (linear matrix inequality) approach, a delay depended condition is proposed, which ensure the singular system is stochastically stable with H∞ performance. An examples is given to show the effectiveness of the proposed approach.
The Effect of Audit Tenure and Audit Office Size on Financial Reports Quality 
An Applied Study on Saudi Public Companies

The current study aimed to know the effect of the length of audit period and the size of audit firm on the quality of the financial statements applied to Saudi joint stock companies. The study focused specifically on the effectiveness of the length of audit period set by the Saudi regulations as a maximum of five years, with no minimum period. To examine the effect of both the
The multi-physics metawedge: graded arrays on fluid-loaded elastic plates and the mechanical analogues of rainbow trapping and mode conversion

We consider the propagation and mode conversion of flexural-acoustic waves along a fluid-loaded graded array of elastic resonators, forming a metasurface. The multi-physics nature of the problem, coupling two disparate physical systems, brings both challenges and novel features not previously seen in so-called bifunctional metamaterials. In particular, by using an appropriately designed graded array of resonators, we show that it is possible to employ our metasurface to mode-convert sub-sonic surface flexural waves into bulk acoustic waves and vice-versa; transferring energy between two very different physical systems. Whilst the sub-sonic mechanical surface wave is dispersive, the bulk acoustic wave is dispersionless and radiates energy at infinity. We also show that this bifunctional metasurface is capable of exhibiting the classical effect of rainbow trapping for sub-sonic surface waves.
Scalable Robust Safety Filter With Unknown Disturbance Set

Equipping any controller with formal safety guarantees can be achieved by using safety filters. These filters modify the desired control input in the least restrictive way to guarantee safety. However, it is an unresolved issue to construct scalable safety filters without assuming the availability of the disturbance set. In this article, we address this issue by proposing an efficient approach to implementing safety filters. In particular, we perform offline set membership identification to obtain a linear model that is conformant to a finite set of training data. Based on this conformant model, we compute a set-based safe backup controller with a corresponding safe set. Because a new measurement obtained online might invalidate the model conformance, we update the model, the safe backup controller, and the safe set online to restore formal safety guarantees. We use scalable reachability analysis and convex optimization algorithms to perform these updates as quickly as possible. We demonstrate the usefulness and scalability of our safety filter approach using four numerical examples from the literature.