Miracle-dz/UniFAR-FTUs huggingface dataset:
Peridynamics-Based Digital Image Correlation Algorithm Suitable for Cracks and Other Discontinuities

AbstractThis work presents a novel approach to circumventing the difficulties associated with using digital image correlation (DIC) for problems that involve discontinuities like fracture and fragmentation. The proposed method provides a simple means to combine traditional DIC approaches with peridynamics to achieve full-field displacements, even in the vicinity of cracks. In regions where DIC is accurate and effective, the displacement field is determined using a traditional approach. In discontinuous regions, the displacement field is solved for using peridynamics. This study shows that this method can be used successfully for investigating damage in materials such as nuclear graphite and fiber-RC (FRC). It also shows that the proposed method alleviates nonphysical strains in the vicinity of cracks, common to alternative approaches, which detrimentally degrade the quality of postprocessed quantities of interest such as fracture toughness.
Efficiency of Modal Coupling between Graded-index Optical Waveguides

Abstract The coupling efficiency between the fundamental modes of two different graded-index waveguides fabricated by ion exchange in the same glass substrate is calculated by numerical and variational methods. Waveguide parameters that allow the designer to achieve the maximum power transfer through the waveguide boundary are determined. A possible application to the design optimization of waveguide lenses is considered.
Microreplication of laser-fabricated surface and three-dimensional structures

The fabrication of defined surface topographies and three-dimensional structures is a challenging process for various applications, e.g. in photonics and biomedicine. Laser-based technologies provide a promising approach for the production of such structures. The advantages of femtosecond laser ablation and two-photon polymerization for microstructuring are well known. However, these methods cannot be applied to all materials and are limited by their high cost and long production time. In this study, biomedical applications of an indirect rapid prototyping, molding microreplication of laser-fabricated two- and three-dimensional structures are examined. We demonstrate that by this method any laser-generated surface topography as well as three-dimensional structures can be replicated in various materials without losing the original geometry. The replication into multiple copies enables fast and perfect reproducibility of original microstructures for investigations of cell–surface interactions. Compared to unstructured materials, we observe that microstructures have strong influence on morphology and localization of fibroblasts, whereas neuroblastoma cells are not negatively affected.
External field induced tricritical phenomenon in the isotropicnematic phase transition of hard non-spherical particle systems

The effect of static external field is studied on the isotropic–nematic phase transition of a system of hard non-spherical particles (rods or platelets) with negative anisotropic polarizability (susceptabilities). On the basis of Onsager theory, the phase coexistence curve is calculated numerically without any approximation. It is found that a weakly ordered nematic phase (uniaxial planar) is in coexistence with a highly ordered biaxial nematic phase which ends at a tricritical point. In the limit of infinite field strength, the orientations of the particles are confined in a plane perpendicular to the field and continuous isotropic–nematic phase transition takes place.
Crystalline and optical quality of proton exchanged waveguides

In this paper we demonstrate the possibility of obtaining high-quality proton-exchanged (PE) lithium niobate guides by performing the exchange at high temperatures (300°C) in benzoic acid diluted with lithium benzoate. Rutherford backscattering studies suggest that the higher exchange temperatures avoid the production of niobium dislocations, and, IR absorption measurements suggest that the use of lithium benzoate diluted melts prevents the creation of interstitial hydrogen.
Aperture synthesis observations of molecules in CRL 2688

Aperture synthesis observations of the line emission of H(C-13)N, HC3N, HNC, and SiS at 33 mm wavelength toward CRL 2688 are presented. The sizes of the molecular envelopes and the molecular abundances are derived from the observations in terms of a model of molecular extinction. The envelopes are elongated east-west and are almost aligned with the direction of the dust lane of the optical nebula. The departure from spherical symmetry is weak and similar to that of HCN, but significantly different from the thin disk structure found for NH3. The sizes of the molecular emission along the major axis are similar. The pedestal observed in the H(C-13)CN spectra may arise from a bipolar outflow or a variable wind corresponding to a fast wind phase from the central object. The abundances of H(C-13)CN, HC3N, and HNC in CRL 2688 are comparable to the peak abundances of IRC +10216. SiS appears to be deficient by an order of magnitude in CRL 2688 with respect to IRC +10216. 29 refs.
Adaptive sensorless robust control of AC drives based on sliding mode control theory

This paper focuses in the design of a new adaptive sensorless robust control to improve the trajectory tracking performance of induction motors. The proposed design employs the so‐called vector (or field oriented) control theory for the induction motor drives, being the designed control law based on an integral sliding‐mode algorithm that overcomes the system uncertainties. This sliding‐mode control law incorporates an adaptive switching gain in order to avoid the need of calculating an upper limit for the system uncertainties. The proposed design also includes a new method in order to estimate the rotor speed. In this method, the rotor speed estimation error is presented as a first‐order simple function based on the difference between the real stator currents and the estimated stator currents.
Wavelet Based Surface Decomposition with Boundary Continuity

ABSTRACTFor the traditional wavelet based surface decomposition, it is difficult to preserve the boundary continuity with adjacent surfaces. Aiming at this problem, this paper proposes a new algorithm for wavelet based surface decomposition while preserving the boundary continuity. First, a B-spline surface is divided into a boundary part and a non-boundary part, and then the non-boundary one is decomposed into a scale part and a wavelet part by wavelet transform. Finally, the T-spline is utilized to reconstruct the boundary one and the scale one. Compared to the traditional wavelet based surface decomposition method, in the new approach the result surface is a T-spline which can preserve the boundary continuity with adjacent surfaces. Several examples and results are given to demonstrate the effectiveness of this approach.
Angle-resolved photoemission study of the graphite intercalation compound KC8: A key to graphene

Electrons in isolated graphene layers are a two-dimensional gas of massless Dirac Fermions. In realistic devices, however, the electronic properties are modified by elastic deformations, interlayer coupling and substrate interaction. Here, we unravel the electronic structure of noninteracting, doped graphene layers by revisiting the stage one graphite intercalation compound ${\text{KC}}_{8}$. To this end we apply angle-resolved photoemission spectroscopy and ab initio calculations. The full experimental dispersion is in excellent agreement with calculations of doped graphene once electron correlations are included at the $GW$ level (Greens function $G$ of the Coulomb interaction $W$). This highlights that ${\text{KC}}_{8}$ has negligible interlayer coupling allowing us to access the full experimental Dirac cone.
Broadband modulation instability gain in a high-birefringence photonic crystal fiber with two zero-dispersion wavelengths

Based on a designed high-birefringence photonic crystal fiber with two zero-dispersion wavelengths (ZDWs), the effect of fiber dispersion, pump power and pump wavelength on the modulation instability (MI) gain in the anomalous dispersion region is comprehensively analyzed in this paper. An asymmetric and broadband MI gain spectrum appears at the anti-Stokes band due to the different ZDWs along the fiber slow- and fast-axes. An optimal MI gain can be obtained when the pump wavelength (1675 nm) is slightly shorter than the second ZDW (1683 nm) and the pump power is 280 W. The optimal MI gain bandwidth finally reaches 152 nm and the gain profile is smoother.
Association of ß-glucuronidase activity with menopausal status, ethnicity, adiposity, and inflammation in women

β-Glucuronidase was positively associated with Hispanic ethnicity, C-reactive protein, body mass index and total fat mass, but not menopausal status nor lean mass, with future studies required to understand mechanistic underpinnings for these findings. Abstract Objectives Many dietary polyphenols with potential health-promoting benefits undergo hepatic conjugation and circulate as inactive glucuronides that can be cleaved by ß-glucuronidase to reform the bioactive aglycone. Although indirect evidence suggests estrogen may induce ß-glucuronidase, little is known about ß-glucuronidase regulation across women's reproductive lifespan. Correlates of serum ß-glucuronidase activity in healthy premenopausal versus postmenopausal women were therefore examined. Methods ß-Glucuronidase activity and C-reactive protein (CRP) were assayed in stored serum from the Women's Breast and Bone Density Study, and dual-energy x-ray absorptiometry and anthropometry assessed body composition. Participants were premenopausal (n = 133) or postmenopausal (n = 89), and Hispanic (37%) or non-Hispanic White (63%). Multivariate linear regression models tested associations between ß-glucuronidase and menopausal status, ethnicity, CRP, and body composition metrics, overall and stratified by menopausal status. Results Postmenopausal (vs premenopausal) women were older (60.4 ± 3.7 vs 44.8 ± 2.4 y) with a lower Hispanic ethnicity prevalence (27% vs 44%), and higher serum ß-glucuronidase activity (1.5 ± 0.8 vs 1.3 ± 0.5 U/L) and CRP (4.2 ± 4.4 vs 3.3 ± 4.7 mg/L). Adjusting for confounders, ß-glucuronidase was positively associated with Hispanic ethnicity, CRP, body mass index, and total fat mass (all, P < 0.01), but not menopausal status nor lean mass. Central adiposity measures were also positively associated with ß-glucuronidase with the same covariates. Conclusions ß-Glucuronidase enzyme activity, upon which polyphenol health-related benefits may depend, is not associated with menopausal status. Future studies are required to determine clinical significance and mechanisms driving ß-glucuronidase associations with ethnicity, inflammation, and adiposity in women.
Psychometric properties and validity of the Menopause Rating Scale in postmenopausal Portuguese women

The Portuguese Menopause Rating Scale has been shown to be a valid and reliable questionnaire for assessing the severity of menopausal symptoms and discriminating among postmenopausal women with and without anxiety and depression. Abstract Objective The aim of the study is to analyze the reliability and validity of the Portuguese version of the Menopause Rating Scale (MRS) among postmenopausal Portuguese women. Methods A total of 184 postmenopausal women completed the Portuguese MRS. Internal consistency and test-retest reliability were studied. Construct, convergent, and concurrent validity were assessed. The 10-item Cervantes Scale and the Hospital Anxiety and Depression Scale were used to determine convergent and concurrent validity, respectively. The ability and accuracy of the Portuguese MRS to discriminate between women with and without anxiety and depression cases were evaluated, and a receiver operating characteristic curve analysis was used. Results The Portuguese MRS showed an appropriate level of internal consistency (Cronbach α, 0.84 for MRS total score) and test-retest reliability (MRS total score; intraclass correlation coefficient, 0.92; 95% confidence interval, 0.85–0.96). Factor analysis (construct validity) revealed a 3-factor structure (explained variance of 62.08%). The MRS total score and its 3 factors showed good convergent (10-item Cervantes Scale) and concurrent (Hospital Anxiety and Depression Scale anxiety and depression) validity (all P < 0.001). The Portuguese MRS total score was significantly able to discriminate between postmenopausal women with and without anxiety (P < 0.001) and depression (P = 0.001), with a cutoff point of 15.50 (80.00% sensitivity and 63.70% specificity) for detecting depression and a cutoff point of 16.50 (82.35% sensitivity and 78.57% specificity) for identifying anxiety. Conclusions The Portuguese MRS has been shown to be a valid and reliable questionnaire for assessing the severity of menopausal symptoms and discriminating among postmenopausal women with and without anxiety and depression.
Lightweight Intrusion Detection Using Multiple Entropies of Traffic Behavior in IoT Networks

Since Mirai malware first appeared in 2016, different variants have been created. The variants infect Internet of Things (IoT) devices such as home routers and webcams. The scale of DDoS attacks using Mirai-infected IoT devices has exceeded 600 Gbps. There has been a lot of researches on intrusion detection methods using machine learning for IoT networks. However, the existing method needs a lot of computational resources. Therefore, it is difficult to run such intrusion detection systems on resource-limited IoT gateways. In this research, we focus on the communication behavior of IoT devices, such as periodic communication with a specific server during benign operations. We propose a new intrusion detection method that represents the communication behavior of each host using multiple entropy features such as destination port number, source port number, and transmission time interval. The proposed method can achieve performance comparable to the existing intrusion detection method even if using a lightweight machine learning algorithm with fewer features. The evaluation of the results shows that the proposed method can reduce the detection processing time by 28.7 ms and memory usage by up to 331 MiB compared to the existing method, and the proposed method can achieve a detection accuracy of 99.8%, which is almost the same as the existing method.
Pixel sensitivity variations in a CdTe-Medipix2 detector using poly-energetic x-rays

We have a 1-mm-thick cadmium telluride (CdTe) sensor bump-bonded to a Medipix2 readout chip. This detector has been characterized using a poly-energetic x-ray beam. Open beam images (i.e. without an attenuating specimen between the x-ray source and the detector) have been acquired at room temperature using the MARS-CT system. Profiles of various rows and columns were analyzed for one hundred, 35-ms exposures taken with a bias voltage of -300 V (operating in electron collection mode). A region of increased sensitivity is observed around the edges of the detector. A reasonably periodic, repeatable variation in pixel sensitivity is observed. Some small regions with very low sensitivity and others with zero signals are also observed. Surrounding these regions are circular rings of pixels with higher counts. At higher flux (higher tube current in the x-ray source) there is evidence of saturation of the detector assembly. In this paper we present our understanding of the origin of these features and demonstrate the improved image quality obtained after correcting for these variations.
A Proxemic Potential Field Approach for Modeling Interactions Between Autonomous Vehicles with Pedestrians and Cyclists

Autonomous vehicles (AVs) are designed to drive in urban areas with a high volume of people on the street. In these locations, the safety of people around and inside the vehicle relies on its capacity to react to possible collisions. Typically, the motion of the obstacles involved follows a static or well-known trajectory, but the people instead can change freely their movement with time, therefore, AVs should consider them as dynamic obstacles. In this work a proxemics-based model is developed to represent in real time the interaction zones, between AVs and pedestrians or cyclists, through elliptic regions depending on instantaneous motion parameters. The model was implemented in simulations involving pedestrians and cyclists walking in street-based scenarios. The results indicate that the proposal allows to detect and break a vehicle in response to a possible collision.
Smart Robot Vision System for Plant Inspection for Disaster Prevention

Simulation of emergency response scenarios and routine inspections are imperative means in ensuring the proper functioning and safety of power plants, oil refineries, iron works, and industrial units. By utilizing autonomous robots, moreover, the reliability and frequency of such inspections can be improved. With the exception of facilities located in hazardous areas, such as off-shore factories, where dispatching response teams might be impossible, accidents caused by human mistakes can be prevented by autonomous inspections and diagnosis of facilities (pumps, tanks, boilers, and so on). One of the primary obstacles in robot-assisted inspection operations is detecting various types of gauges, reading them, and taking appropriate action. This study describes a unique robot vision-based plant inspection system that may be used to enhance the frequency of routine checks and, in turn, minimize equipment faults and accidents (explosions or fires caused by gas leaks) caused by human mistakes or natural degradation. This suggested system can conduct facility inspections by detecting and reading a variety of gauges and issuing reports upon the detection of any anomalies. Furthermore, this system is capable of responding to unforeseen anomalous events that pose potential harm to human response teams, such as the direct manipulation of valves in the presence of a gas leak.
Ethereum Price Prediction using Topological Data Analysis

The popularity of cryptocurrencies is increasing, as they have become an economy of their own, due to the observed returns of investments in cryptocurrencies and digital assets. This led to an increasing interest in the prediction of their prices over the past few years. Ethereum is one of the most popular cryptocurrencies that has witnessed an increase of prices since 2015 while having the second largest market cap. Ethereum is a decentralized platform that incorporates several interactions, not limited to asset trading only; it is also a platform of smart contracts execution and token trading. In this work, we aim at reflecting the interactions perceived in the Ethereum network on Ether prices using Topological Data Analysis (TDA). We introduce a method to extract the TDA features of the indicators of different interaction networks; traded volumes, smart contracts, and transactions between accounts. We conducted an analysis of the effect of using TDA features on Ether price prediction and extended our method to predict the prices of eight Ethereum tokens. Our method resulted in 0.75%, 4.9%, and 13.75% MAPE in hourly, daily, and weekly forecasts respectively, outperforming the previously reported results.
MPPT of a PV panel based on fuzzy logic and sliding mode controller

In this work we present a sliding mode controller to control a boost DC-DC converter inserted in a photovoltaic system. The controller includes a combination between Fuzzy logic supervisor and sliding mode control to extract maximum power from PV systems. The proposed algorithms is compared with conventional algorithms such as the Perturb and observe (P&O) algorithm, incremental conductance (InC) and also unconventional algorithms such as FLC while varying irradiation. The simulation results confirm the performance and its robustness compared to other controllers.
Fuel Efficiency of Garbage Truck Navigation

We investigate achieving fuel efficiency in an economy of garbage truck collections. Fuel efficiency relates to minimising both fuel consumption and travel time. Minimising just the fuel consumption is often done by garbage collection companies and minimising just the travel time is the most popular Google Maps heuristic. We combine these two criteria for online fuel-efficiency optimisation. We prove that this takes exponential time in some edge cases but polynomial time in many practical cases. We also design three natural heuristics, approximating fuel efficiency in the edge cases, that minimise fuel consumption, travel time, and both, respectively. We evaluate the performance of these heuristics in preliminary simulations. Finally, we discuss a plan for the possible deployment of these methods.
Multi-View Contrastive Learning from Demonstrations

This paper presents a framework for learning visual representations from unlabeled video demonstrations captured from multiple viewpoints. We show that these representations are applicable for imitating robotic tasks. We use contrastive learning to enhance task-relevant information while suppressing irrelevant information in the feature embeddings. We validate the proposed method on the publicly available Multi-View Pouring and a custom Pick and Place data sets and compare it with the TCN and CMC baselines. We evaluate the learned representations using three metrics: viewpoint alignment, stage classification and reinforcement learning. In all cases, the results improve when compared to state-of-the-art approaches.
Towards data-driven autonomous cyber defence for military unmanned vehicles - threats & attacks

Unmanned vehicles with varying degrees of autonomy will likely change the way military operations can be conducted, but they also introduce risks that require new ways of thinking security. In particular, the safety ramifications of cyber attacks should be seen as equally critical as the loss of classified data. Developing a cyber defence capability that can detect and manage these potentially harmful events also without human intervention thus becomes a fundamental requirement. In this paper, we commence such work by exploring how to disrupt the functionality of an actual military unmanned ground vehicle given an internal attacker, and how the resulting data can be used to design an an effective detection capability.
Comparative Performance Analysis of PI Controller and MRAC for a Differential Drive Robot

This paper compares the performance of two control techniques on a DC motor-driven differential drive robot. Regular cascaded PI controller is the first scheme. In the second scheme, the outer PI controller is replaced by direct model reference adaptive control (MRAC). The main objective of the motor controller is that it follows a desired angular speed. Both schemes are compared in terms of average absolute speed error and power consumption. Preliminary simulations are performed on a DC motor model; whereas, experimental runs are done on a differentially driven robot powered by DC motors, to compare the performance of both control laws. It is found that the control scheme with MRAC has lesser speed error and power consumption compared to the PI controller, both in simulation and the experimental investigation.