Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential Role of Natural Antioxidant Products in Oncological Diseases

Nutrition has a significant effect and a crucial role in disease prevention. Low consumption of fruit and vegetables and a sedentary lifestyle are closely related with the onset and development of many types of cancer. Recently, nutraceuticals have gained much attention in cancer research due to their pleiotropic effects and relatively non-toxic behavior. In fact, although in the past there have been conflicting results on the role of some antioxidant compounds as allies against cancer, numerous recent clinical studies highlight the efficacy of dietary phytochemicals in the prevention and treatment of cancer. However, further investigation is necessary to gain a deeper understanding of the potential anticancer capacities of dietary phytochemicals as well as the mechanisms of their action. Therefore, this review examined the current literature on the key properties of the bioactive components present in the diet, such as carotenoids, polyphenols, and antioxidant compounds, as well as their use in cancer therapy. The review focused on potential chemopreventive properties, evaluating their synergistic effects with anticancer drugs and, consequently, the side effects associated with current cancer treatments.
Tsunami scour around a cylinder

A series of scale-model experiments investigated the scouring mechanisms associated with a tsunami impinging on a coastal cylindrical structure. Since scaling effects are significant in sediment transport, a large-scale sediment tank was used. Video images from inside the cylinder elucidated the vortex structures and the time development of scour around the cylinder. The scour development and mechanisms differed according to the sediment substrate – sand or gravel. For gravel, the most rapid scour coincided with the greatest flow velocities. On the other hand, for the sand substrate, the most rapid scour occurred at the end of drawdown – after flow velocities had subsided and shear stresses were presumed to have decreased. This behaviour can be explained in terms of pore pressure gradients. As the water level and velocity subside, the pressure on the sediment bed decreases, creating a vertical pressure gradient within the sand and decreasing the effective stress within the sand. Gravel is too porous to sustain this pressure gradient. During drawdown, the surface pressure decreases approximately linearly from a sustained peak at $\uDelta P$ to zero over time $\uDelta T$. The critical fraction $\Lambda $ of the buoyant weight of sediment supported by the pore pressure gradient can be estimated as \[ \Lambda = \frac{2}{\sqrt \pi} \frac{\uDelta P}{\gamma_b \sqrt {c_v \uDelta T}}, \] in which $\gamma_{b}$ is the buoyant specific weight of the saturated sediment and $c_{v}$ is the coefficient of consolidation. Much deeper scour was observed where $\Lambda $ exceeded one-half.
Deep Reinforcement Learning for Online Vulnerability Identification in Smart Grid under Load Uncertainty

The outages of transmission lines can trigger cascading failure in power grid, resulting in serious negative impacts. Identifying those critical lines can help to take precautionary measures and build robust power system. A group of existing works have developed approaches to identify those critical lines. However, the approaches haven’t consider the load uncertainty, which will lead to the changes of critical lines. In this paper, we investigate a critical line identification problem in smart grid considering load uncertainty. Specifically, we first formulate an optimal virtual attacking problem and its objective is to maximize the expected generation loss under the given attacking resources. Due to the existence of large solution space, unknown system dynamics model, and load uncertainty, solving the formulated problem is challenging. Thus, we design an online algorithm based on prioritized multi-agent-attention-actor-critic (PMA3C). Compared with optimization-based methods and evolutionary methods, the designed algorithm can identify critical lines instantly under load uncertainty without searching the whole or partial solution space. Simulation results indicate that the designed algorithm can identify critical lines efficiently.
An Aircraft Towing and Taxiing System Based on Pilot Control Mode

The evolving aviation industry places higher demands on the aircraft towing and taxiing process, which relies heavily on the cooperation between the pilot and the tractor driver traditionally because there is no information exchange between the aircraft and the tractor. An aircraft towing and taxiing system based on pilot control mode is proposed aiming at the inefficiency and complexity of the above-mentioned work mode. An angle sensor is installed between the nose landing gear and the tractor, the tractor can perceive the direction that the pilot wants to go when the pilot is steering the steering wheel, and tow the aircraft forward along its heading angle automatically. According to the simulation and experiments, it is verified that the proposed pilot can complete the aircraft towing and taxiing operation through the active perception and autonomous control of the tractor, which solves the problem of no information interaction between the aircraft and the tractor.
Relative error model reduction via time-weighted balanced stochastic singular perturbation

A new mixed method for relative error model reduction of linear time-invariant systems is proposed in this paper. This order reduction technique is mainly based upon the time-weighted balanced stochastic model reduction method and singular perturbation model reduction technique. Compared to the other analogous counterparts, the proposed method provides more accurate results in terms of time-weighted norms when applied to the practical examples. It is shown that important properties of the time-weighted stochastic balanced reduction technique are extended to the mixed reduction method by using the concept and properties of the reciprocal systems. The results are further illustrated by two practical numerical examples: a model of compact disc player and a model of the atmospheric storm track.
Intrinsic and Extrinsic Calibration of Roadside LiDAR and Camera

LiDAR-camera intrinsic and extrinsic parameters calibration is a prerequisite for LiDAR-camera sensor suite in the cooperative vehicle-infrastructure system or autonomous driving system. However, the calibration of LiDAR-camera is not trivial in some special scenes such as traffic scenes since the traditional target-based calibration methods are subject to the road condition. Moreover, the changing intrinsic parameters of the camera owing to focusing also pose unique challenges to such calibration tasks. Hence, a novel targetless method that jointly performs the intrinsic and extrinsic parameters calibration of LiDAR and camera sensor suite is proposed in this paper. The 2D and 3D features are collected from color images and point cloud respectively. The distance transform image is obtained from a 2D feature map and used as a reprojection error energy function for optimization. Alternatively, the observability of intrinsic and extrinsic parameters in our calibration system is theoretically verified, and the proposed method is tested in simulation and real-world scenes. Experiment results show that the method has better robustness and accuracy than other targetless calibration methods.
On the delamination detection in composite beams with active piezoelectric sensors using non-linear ultrasonics

This paper investigates the potential of a novel SHM method for the detection of delamination cracks in composites which exploits the nonlinear ultrasonic response with in-situ d31 piezoceramic actuators and sensors. Composite beam specimens with artificially created delamination cracks are tested, entailing two piezoceramic actuator patches, the first to generate a low frequency, high power modal excitation and the second a high frequency acoustical wave, as well as a piezoceramic sensor. Nonlinearities induced at the high-frequency signal, such as sidebands at the spectral components as long as modulations at the measured sensory voltage are evaluated as damage indicators. Experimental results quantify the potential of the method in detecting small delamination cracks through spectral sideband components. The influence of high-frequency on the effectiveness of the method is shown. Additionally, the effect of the magnitude of applied voltage on the low frequency actuator on the formation of spectral components is investigated. Finally, the obtained results of the present method are compared with a guided wave based pitch and catch SHM method using the same actuator-sensor pair to excite and monitor the propagation of the first symmetric and asymmetric Lamb waves.
Multiscale Deep Features and Empirical Features Fusion-based Gearbox Fault Diagnosis

The application of gearbox intelligent fault diagnosis techniques in engineering practice is still challenging. Based on a deep neural network, the accuracy and generalization ability of the diagnosis model cannot be guaranteed when the labeled data is insufficient. In addition, due to the complex internal structure and strong coupling of gearbox, it is unrealistic to obtain reliable and complete fault information with a single model structure. Therefore, this paper proposes a fault diagnosis method for gearbox based on the fusion of multiscale deep features and empirical features, which can extract available information from various scales. The method combines traditional signal processing methods, multiscale learning and classical convolutional neural network (CNN) structure, which has two advantages. 1) Taking full advantage of the feature extraction of different techniques, multiscale learning strategies can obtain complementary diagnostic information to improve the diagnostic effect. 2) Variational mode decomposition (VMD) can obtain relatively stable signals, effectively dealing with the data noise problem that helps mine the fault characteristics of vibration signals. The performance on gearbox datasets demonstrates the superiority and robustness of the proposed method in gearbox fault diagnosis.
Few-shot gearbox fault diagnosed based on meta-learning and time convolution network

In order to resolve the problem that the traditional fault diagnosis methods can not accurately diagnose under complex working conditions and few fault data, a fault diagnosis method based on meta-learning and time convolution network (TCN) is proposed in this paper. Firstly, Ensemble Empirical Mode Decomposition-Independent Component Analysis (EEMDICA) is used to denoise the original signals under different working conditions, and the acquired signals are randomly sampled to form meta tasks according to the meta-learning protocol. Through the TCN model training, the priori knowledge is obtained by optimizing the initialization parameters. The knowledge is used to improve accuracy in unforeseen condition tasks. Finally, simulation experiments are carried out on the gearbox dataset. The results indicate that the method has advantages in solving the fault classification problem of few shot gearbox under complex working conditions.
Modification of the TRP Channel TRPA1 as a Relevant Factor in Migraine-Related Intracranial Hypersensitivity

Recently, the transient receptor potential ankyrin 1 (TRPA1) has gained more attention in migraine-related research. The involvement of the TRPA1 receptor in migraine headaches is proposed by the fact that TRPA1 may be a target of some migraine-triggering factors. Although it is doubtful that activation of TRPA1 alone is sufficient to induce pain, behavioral studies have demonstrated that TRPA1 is involved in injury- and inflammation-induced hypersensitivity. Here, we review the functional relevance of TRPA1 in headaches and its therapeutic potential, mainly focusing on its role in the development of hypersensitivity, referring to its altered expression in pathological conditions, and its functional interaction with other TRP channels.
Systematic review of acupuncture to improve ovarian function in women with poor ovarian response

Objective To determine the effect of acupuncture in treating poor ovarian response (POR). Methods We searched MEDLINE (via PubMed), EMBASE, Allied and Complementary Medicine Database, CNKI, CBM, VIP database, Wanfang Database, and relevant registration databases from inception to January 30, 2023. In this review, both Chinese and English peer-reviewed literature were included. Only randomized controlled trials (RCTs) using acupuncture as an intervention for POR patients undergoing in vitro fertilization were considered. Results Seven clinical randomized controlled trials (RCTs) were eventually included for comparison (516 women). The quality of included studies was generally low or very low. For the meta-analysis, seven studies showed that compared with controlled ovarian hyperstimulation (COH) therapy, acupuncture combined with COH therapy could significantly increase the implantation rate (RR=2.13, 95%CI [1.08, 4.21], p=0.03), the number of oocytes retrieved (MD=1.02, 95%CI [0.72, 1.32], p<0.00001), the thickness of endometrium (MD=0.54, 95%CI [0.13, 0.96], p=0.01), and the antral follicle count (MD=1.52, 95%CI [1.08, 1.95], p<0.00001), reduce follicle-stimulating hormone (FSH) levels (MD=-1.52, 95%CI [-2.41, -0.62], p=0.0009) and improve estradiol (E2) levels (MD=1667.80, 95%CI [1578.29, 1757.31], p<0.00001). Besides, there were significant differences in the duration of Gn (MD=0.47, 95%CI [-0.00, 0.94], p=0.05) between the two groups. However, no statistical variation was observed in improving clinical pregnancy rate (CPR), fertilization rate, high-quality embryo rate, luteinizing hormone (LH) value, anti-mullerian hormone (AMH) value, or reducing the dose of gonadotropin (Gn) values between the acupuncture plus COH therapy group and the COH therapy group. Conclusion Acupuncture combined with COH therapy is doubtful in improving the pregnancy outcome of POR patients. Secondly, acupuncture can also improve the sex hormone level of POR women, and improve ovarian function. Furthermore, more RCTs of acupuncture in POR are needed to be incorporated into future meta-analyses. Systematic review registration PROSPERO, identifier CRD42020169560.
On nonexistence of boundary arcs in control problems with bounded state variables

When solving optimal control problems with bounded state variables, one must determine whether the optimal trajectory intersects the boundary only at isolated points in time (boundary point) or remains on the boundary for a nonzero length of time (boundary arc). Previously, this determination has been made by trial and error. The task is complicated by the fact that the necessary conditions in common use for these problems assume that the solution has a boundary arc, and can thus yield a boundary arc when the solution has no boundary arc. In this paper the necessary conditions of [1] are used to derive conditions under which the optimal trajectory cannot have a boundary arc. These conditions include the condition for no boundary arcs developed in [1] as a special case. The application of these conditions is illustrated via several examples.
Low Rank Aligning Two Point Sets for Unsupervised Correcting GPS points of Parking-slots

Parking management is an important part of urban management. Accurate Global Positioning System (GPS) points of parking-slots are the core of various applications ranging from looking for vacant parking-slots to designing the parking policies. Affected by high-rise buildings and various signals, either blocked or affected GPS points tend to mismatch the actual locations. Therefore, due to the large number of parking-slots, it is not a trivial problem to correcting raw GPS points in a unsupervised approach. In this paper, discovering that the parking-slots always most parallel with the roads in a city, we proposes an unsupervised low rank method to effectively correct GPS points of the parking-slots by rotating-translating-aligning operations. The proposed method theoretically handles any errors of GPS points; besides, the proposed unconventional alignment method is simple, yet effective and efficient for large-scale points for a city. Extensive experiments demonstrate the superiority of the proposed method.
The Ergodicity and Sensitivity of Nonautonomous Discrete Dynamical Systems

Let (E,h1,∞) be a nonautonomous discrete dynamical system (briefly, N.D.D.S.) that is defined by a sequence (hj)j=1∞ of continuous maps hj:E→E over a nontrivial metric space (E,d). This paper defines and discusses some forms of ergodicity and sensitivity for the system (E,h1,∞) by upper density, lower density, density, and a sequence of positive integers. Under some conditions, if the rate of convergence at which (hj)j=1∞ converges to the limit map h is “fast enough” with respect to a sequence of positive integers with a density of one, it is shown that several sensitivity properties for the N.D.D.S. (E,h1,∞) are the same as those properties of the system (E,h). Some sufficient conditions for the N.D.D.S. (E,h1,∞) to have stronger sensitivity properties are also presented. The conditions in our results are less restrictive than those in some existing works, and the conclusions of all the theorems in this paper improve upon those of previous studies. Thus, these results are extensions of the existing ones.
Remote Sensing Image Recovery and Enhancement by Joint Blind Denoising and Dehazing

Due to the hazy weather and the long-distance imaging path, the captured remote sensing image (RSI) may suffer from detail loss and noise pollution. However, simply applying dehazing operation on a noisy hazy image may result in noise amplification. Therefore, in this article, we propose joint blind denoising and dehazing for RSI recovery and enhancement to address this problem. First, we propose an efficient and effective noise level estimation method based on quad-tree subdivision and integrate it into fast and flexible denoising convolutional neural network for blind denoising. Second, a multiscale guided filter decomposes the denoised hazy image into base and detailed layers, separating the initial details. Then, the dehazing procedure using the corrected boundary constraint is implemented in the base layer, while a nonlinear sigmoid mapping function enhances the detailed layers. The last step is to fuse the enhanced detailed layers and the dehazed base layer to get the final result. Using both synthetic remote sensing hazy image (RSHI) datasets and real-world RSHI, we perform comprehensive experiments to evaluate the proposed method. Results show that our method is superior to well-known methods in both dehazing and joint denoising and dehazing tasks.
Structures and stability of defect-free multiwalled carbon toroidal rings

Atomistic simulations of the structures and stability of defect-free multiwalled carbon toroidal rings were performed using the second-generation empirical bond-order potential and a Morse-type van der Waals potential. It was found that a multiwalled toroidal ring improves the structural stability over its outermost single-walled counterpart, implying a stabilizing effect from the inner rings. This can be explained by the superlinear relation between the critical ring diameter and its tube diameter existing in single-walled rings. However, the findings that the critical diameter of an armchair ring is larger than that of a zigzag ring with the same tube diameters, and that the inclusion of torsion exhibits a negative effect on the stability of a multiwalled ring, are in contrast to that of a single-walled nanoring. In addition, the instability of a multiwalled nanoring always starts with the formation of many short-wavelength ripples on the compressed side of the outermost tube. Subsequently, some of the .
Stability of fuzzy linguistic control systems

A new approach to the stability analysis of fuzzy linguistic control (FLC) systems is presented. Specifically, it is shown that the direct method of Lyapunov can be used to determine sufficient conditions for global stability of a broad class of fuzzy control schemes. Moreover, a measure of robustness is proposed that can be used to evaluate and possibly redesign a given fuzzy control system so as to enhance the range of its stable operation. Finally, the application of the proposed methodology is shown and its implications in terms of control design are demonstrated by means of numeric examples. <<ETX>>
Dimethylammonium cation stabilizes all-inorganic perovskite solar cells

1State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Materials Science and Engineering, Hebei University of Technology, Tianjin 300401, China 2Key Laboratory of Optoelectronic Technology & Systems (MoE), College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China 3Center for Excellence in Nanoscience (CAS), Key Laboratory of Nanosystem and Hierarchical Fabrication (CAS), National Center for Nanoscience and Technology, Beijing 100190, China 4Macao Institute of Materials Science and Engineering (MIMSE), Macau University of Science and Technology, Macau 999078, China
Constructal Theory Of Organization In Nature: Dendritic Flows, Allometric Laws And Flight

This paper draws attention to the 1996 constructal theory of the generation of geometric form in flow systems. Flow architecture can be reasoned on the basis of principle: the maximization of global performance subject to finite-size constraints. One example is the generation of tree-shaped flow patterns, as optimized paths between one point (source, sink) and an infinity of points (area, volume), The optimized tree-flow architecture accounts for allometric laws, for example, the proportionality between metabolic rate and body size raised to the power 3/4, and the proportionality between breathing and heart beating times and body size raised to the power 1/4. Another example is the proportionality between the cruising speed of flying bodies (insects, birds, airplanes) and body mass raised to the power 1/6, The “thermodynamics law” status of the constructal principle is discussed. 1 Constructal theory versus biomimetics Geometric similarities and patterns abound in flow systems in engineering and in nature. For example, tree-shaped flows are everywhere, in computers, lungs, dendritic crystals, urban street patterns, and communication links, In a new book [1], I started from the design and optimization of engineered systems and developed a deterministic principle for the generation of geometric form in natural systems. In flow systems for fluid, heat, mass, electricity, goods, and traffic, better performance means improved access: minimal flow resistance, minimal travel time, minimal cost. This observation led to constructal theo~,
Editorial: The role of sex in heart failure and transplantation, volume II

COPYRIGHT © 2023 Ayesta, Díaz-Molina, Bayes-Genis, Baranchuk and Martínez-Sellés. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: The role of sex in heart failure and transplantation, volume II