Miracle-dz/UniFAR-FTUs huggingface dataset:
Extending Quadrotor Motion Capability by Centrally Coaxial Tilting Rotors

Motion of the typical quadrotor is restricted since it employs only four fixed rotors and thus underactuated. We extend its capability by introducing the coaxial tilting rotors that can generate thrust force with lateral component. They are installed at the center of the body frame with a gimbal mechanism to control the thrust direction. The original and new rotors together produce control force and torque that make attitude stabilizing, tilting, and sideway translation possible. Based on contraction analysis, we propose a nonlinear PID controller for this new UAV to follow admissible desired trajectories. The new special integral error term addresses modeling uncertainties, loss of thrust force, and disturbances. Moreover, we apply optimization technique to allocate rotor thrusts considering the gimbal dynamics and rotor speed limit. Simulation and experiments are conducted to compare the effectiveness of the special integral error, validate the range of achievable motion, and demonstrate the tracking robustness and motion tracking on a 3D-Lissajous curve.
Global Dynamics of Pipes Conveying Pulsating Fluid in the Supercritical Regime

Global dynamics of supercritical pipes conveying pulsating fluid considering superharmonic resonance of the second mode with 1:2 internal resonance are investigated. The governing partial differential equations in the supercritical regime are obtained based on the nontrivial equilibrium configuration of the pipes conveying fluid and then transformed into a discretized nonlinear gyroscopic system via assumed modes and Galerkin’s method. The method of multiple scales and canonical transformation are applied to reduce the equations of motion to the near-integrable Hamiltonian standard form. The energy-phase method is employed to demonstrate the existence of chaotic dynamics by identifying the existence of multi-pulse jumping orbits in the perturbed phase space. The global solutions are subsequently interpreted in terms of the physical motion of such gyroscopic system. Two types of nonlinear normal modal motion and the chaotic pattern conversion between the locked simple bidirectional traveling wave motion an.
Un théorème d'annulation pour des équations elliptiques non linéaires sur des variétés riemanniennes compactes

Soient (M, g) une variete riemannienne compacte, sans bord de dimension n, Δ g le Laplacien de (M, g) et λ 1 sa premiere valeur propre non nulle. Nous supposons que le tenseur de Ricci de g est positif ou nul et soit R sa borne inferieure, Si q ∈] 1, n + 2/n − 2] et λ > 0 et λ > 0 nous explicitons un nombre C = C (n, q, R, λ 1 ) ≥ 0 tel que toute solution positive de (E) Δ g u + u q = λu sur M soit constante des que λ ≤ C
A weighted Poisson distribution and its application to cure rate models

ABSTRACT The family of weighted Poisson distributions offers great flexibility in modeling discrete data due to its potential to capture over/under-dispersion by an appropriate selection of the weight function. In this paper, we introduce a flexible weighted Poisson distribution and further study its properties by using it in the context of cure rate modeling under a competing cause scenario. A special case of the new distribution is the COM-Poisson distribution which in turn encompasses the Bernoulli, Poisson, and geometric distributions; hence, many of the well-studied cure rate models may be seen as special cases of the proposed model. We focus on the estimation, through the maximum likelihood method, of the cured proportion and the properties of the failure time of the susceptibles/non cured individuals; a profile likelihood approach is also adopted for estimating the parameters of the weighted Poisson distribution. A Monte Carlo simulation study demonstrates the accuracy of the proposed inferential method. Finally, as an illustration, we fit the proposed model to a cutaneous melanoma data set.
Stochastic Hybrid Networks for Global Almost Sure Unanimous Decision Making

A stochastic, hybrid algorithm for global almost sure unanimous decision making in multiagent systems is introduced. Homogeneous agents communicate over an undirected connected graph to make a unanimous selection among a finite set of decision states. The agents have inertia corresponding to stable, linear, and continuous-time dynamics. The algorithm equips each agent with a logic variable and designs logic-variable reset rules to ensure eventual unanimity. These resets occur randomly in time. They are randomly assigned among those indices of the decision states that nearly minimize the value of a function that quantifies the mismatch between the average of the agent states, or a local estimate thereof, and the corresponding decision state. In order to satisfy regularity properties that confer robustness, the resulting update rule corresponds to an inclusion, i.e., a set-valued mapping. Global almost sure decision making is established using a classical Lyapunov function argument that has recently been extended to stochastic hybrid inclusions.
Statistical Parametric Mapping The Analysis Of Functional Brain Images

Thank you very much for reading statistical parametric mapping the analysis of functional brain images. As you may know, people have search numerous times for their chosen novels like this statistical parametric mapping the analysis of functional brain images, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some infectious bugs inside their desktop computer.
The glycoside hydrolase gene family profile and microbial function of Debaryomyces hansenii Y4 during South-road dark tea fermentation

Microbes are crucial to the quality formation of Sichuan South-road Dark Tea (SSDT) during pile-fermentation, but their mechanism of action has not yet been elucidated. Here, the glycoside hydrolase (GH) gene family and microbial function of Debaryomyces hansenii Y4 during solid-state fermentation were analyzed, and the results showed that many GH genes being distributed in comparatively abundant GH17, GH18, GH76, GH31, GH47, and GH2 were discovered in D. hansenii. They encoded beta-galactosidase, alpha-D-galactoside galactohydrolase, alpha-xylosidase, mannosidase, etc., and most of the GHs were located in the exocellular space and participated in the degradation of polysaccharides and oligosaccharides. D. hansenii Y4 could develop the mellow mouthfeel and “reddish brown” factors of SSDT via increasing the levels of water extracts, soluble sugars and amino acids but decreasing the tea polyphenols and caffeine levels, combined with altering the levels of thearubiins and brown index. It may facilitate the isomerization between epicatechin gallate and catechin gallate. Moreover, the expression levels of DEHA2G24860g (Beta-galactosidase gene) and DEHA2G08602g (Mannan endo-1,6-alpha-mannosidase DFG5 gene) were sharply up-regulated in fermentative anaphase, and they were significantly and negatively correlated with epicatechin content, especially, the expression of DEHA2G08602g was significantly and negatively correlated with catechin gallate level. It was hypothesized that D. hansenii Y4 is likely to be an important functional microbe targeting carbohydrate destruction and catechin transformation during SSDT pile-fermentation, with DEHA2G08602g as a key thermotolerant functional gene.
NEEDS ANALYSIS FOR THE DEVELOPMENT OF ARDUINO MICROCONTROLLER-BASED HOOKE'S LAW TEACHING TOOL GUIDEBOOK

This research is a type of research and development (R & D) which has a focus on the goal of producing a product and testing its effectiveness and feasibility. This research model adopts the 4D research model with stages namely Define , Design , Develop , and Disseminate . This needs analysis aims to determine what teaching materials are needed on Hooke's Law material at SMA Negeri 1 Bengkulu Tengah. The research was carried out in October 2022. The research sample consisted of 40 students who were in class XI. The research instruments were interviews and student response questionnaires consisting of 18 question items regarding the need for a Hooke's Law visual aids guidebook based on the Arduino microcontroller which contained 4 aspects, namely aspects of student and teacher needs, aspects of the need for teaching materials, aspects of learning media, and aspects of interest/motivation . The results showed that most of the students were interested in developing an Arduino-based Hooke's law teaching aid guidebook.
Impact of Extraction Solvent Polarity: Antioxydant Activity of Methanolic, Hydromethanolic and Aqueous Decocted Extracts of Algerien Thymelaea hirsuta (L.) Endl. Areal Parts

Thymelaea hirsuta (L.) Endl. , known as ‘Methnane’ in Algeria, is a widely medicinal plant used in folk medicine. In the present study, In vitro antioxidant activity of T. hirsta extracts and the impact of extraction solvent polarity on the antioxidant potential were investigated. Three types of polar solvents with decreasing polarity were chosen; water for decocted extract, methanol-water at 50% and absolute methanol for macerated extracts. Total phenolic and flavonoid contents were evaluated and showed a high amount wihch decreases with increasing polarity. Antioxydant activity was assessed with different methods: ABTS assay for evaluation of scavenge activity, CUPRAC and reducing power for assessement of the reduction potential of T. hirsuta areal parts. The results showed that T. hirsuta areal parts exhibited a strong scavenging activity with significant difference between extracts in terms of their polarity. In the same line, the most polar aqueous decocted extract exhibited a considerable reducing activity followed by hydromethanolic and methanolic extract with increasing potential. These findings suggest the suitability of polar solvents for the extraction of phytochemical compounds from T. hirsuta areal parts and so, their antioxidant activity against several radicals and ions.
A Simple Statistical Method for Recognition of Hand-Written Numerals

We explore a simple statistical method for classifying handwritten numerals. We work with numerals only in segmented form. We defined a pseudodistance function between handwritten numerals and classify each numeral based on its distance from the those in the reference set. This method, and its obvious variants, are studied on the basis of their performance on a data-set of moderate size (95 observations). The results suggest that this method may be considered in recognition of handwritten numeric characters.
A stabilised finite element method for a time-dependent problem solved using a fictitious domain method

In this work we present and analyse a stabilised finite element method for a fictitious domain formulation of a transient heat equation posed on an irregular domain. First, the problem is reformulated as a variational problem with constraints in a larger, simpler domain. In order to have freedom to choose the spaces for primal variable and Lagrange multiplier, we introduce a stabilising term whose consistency error can be shown to be of optimal order. We show that the fully discrete problem is stable and enjoys optimal convergence under the extra assumption that the initial condition has been chosen appropriately. This requirement is not linked to the stabilised character of the method, as the numerical experiments confirm.
Sparsity-Aware Orthogonal Initialization of Deep Neural Networks

Deep neural networks have achieved impressive pattern recognition and generative abilities on complex tasks by developing larger and deeper models, which are increasingly costly to train and implement. There is in tandem interest to develop sparse versions of these powerful models by post-processing with weight pruning or dynamic sparse training. However, these processes require expensive train-prune-finetune cycles and compromise the trainability of very deep network configurations. We introduce sparsity-aware orthogonal initialization (SAO), a method to initialize sparse but maximally connected neural networks with orthogonal weights. SAO constructs a sparse network topology leveraging Ramanujan expander graphs to assure connectivity and assigns orthogonal weights to attain approximate dynamical isometry. Sparsity in SAO networks is tunable prior to model training. We compared SAO to fully-connected neural networks and demonstrated that SAO networks outperform magnitude pruning in very deep and sparse networks up to a thousand layers with fewer computations and training iterations. Convolutional neural networks are SAO networks with special constraints, while kernel pruning may be interpreted as tuning the SAO sparsity level. Within SAO framework, kernels may be pruned prior to model training based on a desired compression factor rather than post-training based on parameter-dependent heuristics. SAO is well-suited for applications with tight energy and computation budgets such as edge computing tasks, because it achieves sparse, trainable neural network models with fewer learnable parameters without requiring special layers, additional training, scaling, or regularization. The advantages of SAO networks are attributed to both its sparse but maximally connected topology and orthogonal weight initialization.
Dimensionality-varied deep convolutional neural network for spectral–spatial classification of hyperspectral data

Abstract. Many methods of hyperspectral image classification have been proposed recently, and the convolutional neural network (CNN) achieves outstanding performance. However, spectral–spatial classification of CNN requires an excessively large model, tremendous computations, and complex network, and CNN is generally unable to use the noisy bands caused by water-vapor absorption. A dimensionality-varied CNN (DV-CNN) is proposed to address these issues. There are four stages in DV-CNN and the dimensionalities of spectral–spatial feature maps vary with the stages. DV-CNN can reduce the computation and simplify the structure of the network. All feature maps are processed by more kernels in higher stages to extract more precise features. DV-CNN also improves the classification accuracy and enhances the robustness to water-vapor absorption bands. The experiments are performed on data sets of Indian Pines and Pavia University scene. The classification performance of DV-CNN is compared with state-of-the-art methods, which contain the variations of CNN, traditional, and other deep learning methods. The experiment of performance analysis about DV-CNN itself is also carried out. The experimental results demonstrate that DV-CNN outperforms state-of-the-art methods for spectral–spatial classification and it is also robust to water-vapor absorption bands. Moreover, reasonable parameters selection is effective to improve classification accuracy.
Communicationless Reactance Compensation Using PWM-Controlled Switched Capacitors for Wireless Power Transfer

Self-inductance variations caused by component tolerance and aging decrease the efficiency of wireless power transfer (WPT) systems. This article deals with this issue by compensating for the self-inductance variations using pulsewidth-modulation-controlled switched capacitors. Previous methods relied on wireless communication between the transmitter (Tx) and receiver (Rx) to control the variable capacitors, which is not a feasible approach when considering practical constraints. This article proposes a control scheme in which the switched capacitors are controlled simultaneously by separate closed loops on the Tx and Rx sides, thus eliminating wireless communication. The control scheme is based on a unique condition for perfect resonance derived by focusing on the output power of the WPT circuit. This article also points out the necessity to compensate for the self-inductance variations of both the Tx and Rx coils to achieve maximum ac/ac efficiency in battery charging applications. Furthermore, the proposed control scheme incorporates the soft-switching condition of the inverter to improve the dc/dc efficiency. Experimental results of a 1-kW prototype show that the switched capacitors can flexibly adapt to the self-inductance variations and improve the ac/ac efficiency and dc/dc efficiency by up to 3.04% and 8.63%, respectively.
ICRH assisted breakdown study on JET

Ion cyclotron (IC) wave assisted breakdown has the potential to increase the robustness of plasma initiation during the ITER pre-fusion operation phase. Studies were performed at JET at ITER relevant loop electric field, Eloop≲0.33 Vm−1 , and a range of toroidal fields, including at the low toroidal field of 1.7T for which breakdown had not been achieved previously on JET. The study covered a range of H2 and D2 gas prefill pressures and timings, pumping conditions, and residual impurity levels. IC assisted breakdown was achieved for a lower and wider range of gas prefill pressures. IC assisted breakdown works by activating wall pumping before the current rise, changing the relation between fuelling and torus pressure in this phase compared to Ohmic breakdown. IC assisted breakdown enables plasma initiation with a higher level and significantly wider range of injected plasma prefill gas. As the injected prefill gas is the controlled parameter, this significantly improves the robustness of plasma initiation operationally. IC assistance is found to be more robust at ITER-like Eloop , succeeding with higher low-Z impurity content. Moreover, it does not introduce an impurity source that may hamper the subsequent burn though and current ramp-up phase. For both the IC assisted and pure Ohmic breakdown, the initial current rise rate is found to scale with ne/Eloop . The results and implications for ITER are presented.
Self-consistent long-time simulation of chirping and beating energetic particle modes in JT-60U plasmas

Recurring bursts of chirping Alfvén modes that were observed in JT-60U tokamak plasmas driven by negative-ion-based neutral beams (N-NB) are reproduced in first-principle simulations performed with an extended version of the hybrid code MEGA. This code simulates the interactions between gyrokinetic fast ions and magnetohydrodynamic (MHD) modes in the presence of a realistic fast ion source and collisions, so that it self-consistently captures dynamics across a wide range of time scales (0.01–100 ms). The simulation confirms that the experimentally observed phenomena known as ‘fast frequency sweeping (fast FS) modes’ are caused by bursts of energetic particle modes (EPM) with dominant toroidal mode number n = 1. On the long time scale (1–10 ms), the simulation reproduces the chirping range (40–60 kHz), the burst duration (few ms) and intervals (5–10 ms). On the short time scale (0.01–0.1 ms), it reproduces pulsations and phase jumps, which we interpret as the result of beating between multiple resonant wave packets. Having reproduced at multiple levels of detail the dynamics of low-amplitude long-wavelength Alfvén modes driven by N-NB ions, the next goal is to reproduce and explain abrupt large-amplitude events (ALE) that were seen in the same experiments at longer time intervals (10–100 ms).
Direct laser interference patterning of transparent and colored polymer substrates: ablation, swelling, and the development of a simulation model

It is well known that micro and sub-micrometer periodical structures play a significant role on the properties of a surface. Ranging from friction reduction to the bacterial adhesion control, the modification of the material surface is the key for improving the performance of a device or even creating a completely new function. Among different laser processing techniques, Direct Laser Interference Patterning (DLIP) relies on the local surface modification process induced when two or more beams interfere and produce periodic surface structures. Although the produced features have controllable pitch and geometry, identical experimental conditions applied to different polymers can result on totally different topologies. In this frame, observations from pigmented and transparent polycarbonate treated with ultraviolet (263 nm) and infrared (1053 nm) laser radiation permitted to identify different phenomena related with the optical and chemical properties of the polymers. As a result from the experimental data analysis, a set of material-dependent constants can be obtained and both profile and surface simulations can be retrieved, reproducing the material surface topography after the surface patterning process.
Development of an In Vitro Bioassay System to Examine the Effects of Bioactive Substances on the Chromatophores’ Movement Using the Epidermis of the Bigfin Reef Squid Sepioteuthis lessoniana

___________________________________________________________ Abstract: To examine the neurophysiological regulation of squid chromatophores, an in vitro bioassay system was developed using the epidermis of the bigfin reef squid Sepioteuthis lessoniana . The squid epidermis was sectioned under anesthesia with 0.1% ethanol and stretched over a kimwipe soaked with artificial seawater. In cephalopods, chromatophores have three types of pigments: orange, yellow, and dark brown; however, only chromatophores with dark brown pigment were analyzed in this study. After stabilizing the contraction-relaxation movement of the chromatophore, the following bioactive substances were added to the separated epidermis: acetylcholine,  - melanocyte-stimulating hormone, and noradrenaline (each 1 mM); and the chromatophores ’ movement was observed under a microscope while taking videos. The video-based analysis of squid chromatophores indicated that acetylcholine and  -melanocyte-stimulating hormone had no effect on the squid chromatophores movement under present conditions. Conversely, noradrenaline (1 mM) promoted contraction-relaxation squid chromatophores ’ movement. The accelerated movement of chromatophores was inhibited by the addition of yohimbine (1 mM) but not by propranolol (1 mM). To the best of our knowledge this is the first report determining that noradrenaline promotes contraction-relaxation movement of the chromatophore via  -2 receptors in the bigfin reef squid because yohimbine is known to be an  2-adrenergic receptor blocker. In current study, squid chromatophores responded well to bioactive substances including inhibitors. Collectively, our in vitro bioassay provides an effective model to investigate the mechanism of contraction-relaxation movement of squid chromatophores.
DBCR-YOLO: improved YOLOv5 based on double-sampling and broad-feature coordinate-attention residual module for water surface object detection

Abstract. Unmanned missions have become more and more popular in recent years. The related technologies of unmanned ground vehicles and unmanned aerial vehicles are growing rapidly, but research on unmanned surface vehicles (USVs) is rare. Water surface object detection algorithms play a crucial role in the field of USVs. However, achieving an object detection algorithm that balances speed and accuracy in the presence of interference is a difficult challenge. We proposed a network, DBCR-YOLO, that improved the detection accuracy while meeting real-time requirements. Based on YOLOv5, we added an additional detection head for detecting tiny objects. Then, we replaced the downsampling in YOLOv5’s backbone network with the proposed double sampling mechanism to solve the problem that paying attention to the key features of objects cannot be done in the downsampling process of YOLOv5. Finally, we substituted the proposed BCR neck for YOLOv5’s neck, thus improving the fusion of features between different scales based on fewer parameters and fewer calculations. We tested our network on the water surface object detection dataset. Compared with YOLOv5, DBCR-YOLO improved the detection accuracy by 3.4%. At the same time, DBCR-YOLO achieved the highest accuracy in comparison with other networks.