Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing On-Satellite Data Analysis with Reinforcement Learning

This paper envisions a new data analysis paradigm to analyze the on-satellite data effectively motivated by the increasing privacy concern and computing capacity of nowadays satellites. To facilitate the new paradigm, we built an analytical model that uses a ground server to dispatch tasks to satellites and return aggregated results to analysts. In order to efficiently schedule tasks, we identified the characteristics of satellites in terms of intermittent connections and dynamic load levels, and then proposed a reinforcement learning-based scheduling algorithm. The preliminary simulation result shows that the proposed algorithm can jointly optimize latency and satellite resource consumption under various task arrival intervals.
WiRITE: General and Practical Wi-Fi Based Hand-Writing Recognition

Device-free hand-writing systems identify the content that a user writes by hand movement in the air, thus providing an intuitive human computer interface. In this paper, we propose WiRITE, a Wi-Fi hand-writing recognition system built with commodity Wi-Fi APs. Unlike most existing machine learning based hand-writing recognition systems, which are often subject to severe limitations in generality, e.g., high training overhead when adapted across hand-writing alphabets, environments, and users, WiRITE is designed with unique consideration of its generality when applied to practice—being application-transferable, environment-agnostic, and user-independent. With little training overhead, WiRITE behaves inclusively to different users, environments, and applications, stemming from a comprehensive design of signal processing that is built into its core machine learning model. Extensive evaluation is conducted with five users for three applications, i.e., recognizing Digits, English letters, and Chinese characters, in realistic office environment. The experiment results demonstrate that WiRITE provides at least 0.9 accuracy in various combinations of users and applications with 0.93 accuracy in average.
The CDK4/6 Inhibitor Palbociclib Inhibits Estrogen-Positive and Triple Negative Breast Cancer Bone Metastasis In Vivo

Simple Summary When breast cancer (BC) spreads to the skeleton it is no longer curable; new treatments are needed in this setting. When given daily, the CDK4/6 inhibitor palbociclib significantly impedes tumour growth in murine models of both estrogen receptor positive and triple negative bone metastatic BC. When a treatment break was introduced, mimicking the clinical setting, tumour growth resumed and continued even in the presence of further cycles of palbociclib. In combination treatment with bisphosphonate zoledronic acid, or a CDK7 inhibitor, palbociclib was insufficient in preventing tumour growth. This suggests that tumour cells become insensitive to palbociclib after a treatment break. To explore possible underlying reasons for this, we harvested palbociclib-sensitive and -insensitive tumour cells from bone and found differences in the levels of key proteins that palbociclib affects. We provide the first demonstration that palbociclib is effective at reducing breast tumour growth in bone, if given daily. Abstract CDK 4/6 inhibitors have demonstrated significant improved survival for patients with estrogen receptor (ER) positive breast cancer (BC). However, the ability of these promising agents to inhibit bone metastasis from either ER+ve or triple negative BC (TNBC) remains to be established. We therefore investigated the effects of the CDK 4/6 inhibitor, palbociclib, using in vivo models of breast cancer bone metastasis. In an ER+ve T47D model of spontaneous breast cancer metastasis from the mammary fat pad to bone, primary tumour growth and the number of hind limb skeletal tumours were significantly lower in palbociclib treated animals compared to vehicle controls. In the TNBC MDA-MB-231 model of metastatic outgrowth in bone (intracardiac route), continuous palbociclib treatment significantly inhibited tumour growth in bone compared to vehicle. When a 7-day break was introduced after 28 days (mimicking the clinical schedule), tumour growth resumed and was not inhibited by a second cycle of palbociclib, either alone or when combined with the bone-targeted agent, zoledronic acid (Zol), or a CDK7 inhibitor. Downstream phosphoprotein analysis of the MAPK pathway identified a number of phosphoproteins, such as p38, that may contribute to drug-insensitive tumour growth. These data encourage further investigation of targeting alternative pathways in CDK 4/6-insensitive tumour growth.
Liquid Metal-Based Flexible Sensor for Perception of Force Magnitude, Location, and Contacting Orientation

Flexible sensors have the advantage to enable intelligent systems to interact with the environment with high safety and versatility. Liquid metal is a useful element for flexible sensors due to its stretchable, reconfigurable, and healing characteristics. However, it is difficult to decode the stimuli position using uniform liquid metal because of its homogeneity and fluidity. Current sensing systems for the perception of force magnitude and contact location mostly rely on sensing arrays and massive crossover electrodes. In this article, we proposed a novel method for simultaneously sensing the force magnitude and contact location without crossover electrodes. In the new design, we encapsulate the liquid metal electrodes with varying cross sections in two silicon layers that function as protective skin and govern the geometry of liquid metals. When a force is applied to the flexible sensor, the change of resistance is dependent on both location and stimuli magnitude. Three liquid metal channels with different gradient resistance are embedded in the prototypes with parallel distributions. Combining the signals collected from the three channels, we can simultaneously measure the force magnitude, stimuli location, and contacting orientation. The overall force resolution is sub-Newton in the range from 0.5 to 20 N. The position can also be determined with a 100% success rate for the segmented sensor and a position resolution of 2 mm for the continuous sensor. The error for contact orientation measurement is 4.58° when the orientation ranges from 45° to 135°. The proposed multifunctional sensing device with high accuracy and wide sensing range has significant potential for developing interactive interfaces for human–machine interactions and industrial applications.
Activity of Patchouli and Tea Tree Essential Oils against Staphylococci Isolated from Pyoderma in Dogs and Their Synergistic Potential with Gentamicin and Enrofloxacin

Simple Summary The discovery of antibiotics was a breakthrough in medicine. However, bacterial defense mechanisms driven by genetic variation resulted in resistance to these compounds relatively quickly. Moreover, new classes of antibiotics have not been developed for 30 years. Within the European Union, the EU Parliament and Council Regulation No. 2019/6, which concerns veterinary medicinal products, is currently in force. The current goal is to reduce the use of antibiotics and to stop the rise of drug resistance in bacteria because such antimicrobial resistant organisms can be transmitted to humans through the consumption of animal products or direct contact with animals (dogs, cats, etc.). For this reason, there is a growing interest in essential oils (EOs). As natural mixtures (usually of terpenes and their derivatives), they may consist of about 20–60 components with 1–3 dominant component(s). An important feature of EOs is their hydrophobicity, which allows them to react with lipids present in bacterial cell membranes and mitochondria, disrupting the functioning of cell structures and consequently making them more permeable to other components or antibiotics. In the present manuscript, the activity of two EOs (patchouli and tea tree) was assessed, and their interaction with gentamicin and enrofloxacin was studied. Abstract In this paper, we show the effect of some essential oils (EOs) on staphylococci, including multidrug-resistant strains isolated from pyoderma in dogs. A total of 13 Staphylococcus pseudintermedius and 8 Staphylococcus aureus strains were studied. To assess the sensitivity of each strain to the antimicrobial agents, two commercial EOs from patchouli (Pogostemon cablin; PcEO) and tea tree (Melaleuca alternifolia; MaEO) as well as two antibiotics (gentamicin and enrofloxacin) were used. The minimum inhibitory concentration (MIC) followed by checkerboards in the combination of EO-antibiotic were performed. Finally, fractional inhibitory concentrations were calculated to determine possible interactions between these antimicrobial agents. PcEO MIC ranged from 0.125 to 0.5 % v/v (1.2–4.8 mg/mL), whereas MaEO MIC was tenfold higher (0.625–5% v/v or 5.6–44.8 mg/mL). Gentamicin appeared to be highly prone to interacting with EOs. Dual synergy (38.1% of cases) and PcEO additive/MaEO synergism (53.4%) were predominantly observed. On the contrary, usually, no interactions between enrofloxacin and EOs were observed (57.1%). Both commercial EOs were characterized by natural composition without artificial adulteration. Patchouli and tea tree oils can be good alternatives for treating severe cases of pyoderma in dogs, especially when dealing with multidrug-resistant strains.
Actions des groupes de Lie presque simples et positivité de la p-courbure

H. B. Lawson et S. T. Yau ont demontre dans [6] que si une variete compacte admet une action C∞ effective de SU(2) ou SO(3), alors elle admet une metrique riemannienne a courbure scalaire positive. En supposant que la variete admet une action d'un groupe de Lie compact, connexe, simple et de rang superieur, on obtient une propriete de positivite plus forte.
Arduino Uno Based Swarm Intelligence Robots

Swarm robotics is an emerging study area in which swarm intelligence is applied to multi-robot systems. It investigates ways to employ local rules to coordinate massive groups of relatively simple robots. Its study involves the design of a series of highly simple robots, as well as their physical form and working control. Numerous successful experiments have been carried out and researchers are investigating to improve the working swarm robots. Swarm robotics is based on the creation, deployment, and coordination of large groups of robots to solve a problem or execute a task. The distinctive characteristics of intelligent systems are described in relation to single bots and other number of co-systems after a brief discussion of the nature of swarms and swarm intelligence. Following that, a list of various frequently used swarm robotics entity is shown in various swarm robotics modeling approach. By presenting present scenarios that cannot be resolved by a single bot or a small number of robots operated in a typical, centralized manner, the most feasible industries for swarm implementations and evaluate its potential influence on particular industries. The paper represents on swarm robotic approaches, which includes cooperative control mechanisms in intelligent systems for swarming, navigating, and searching tasks.
Replacement of train wheels: an application of dynamic reversal of a Markov process

A problem of regrinding and recycling worn train wheels leads to a Markov population process with distinctive properties, including a product-form equilibrium distribution. A convenient framework for analyzing this process is via the notion of dynamic reversal, a natural extension of ordinary (time) reversal. The dynamically reversed process is of the same type as the original process, which allows a simple derivation of some important properties. The process seems not to belong to any class of Markov processes for which stationary distributions are known.
Dynamic Incentive for Reliable MCS Participant Selection

Mobile crowdsensing (MCS), which relies on a large number of ordinary participants to complete sensing tasks, has become a new data collection paradigm in the Internet of Things (IoT). In the MCS system, how to select reliable participants to participate in the sensing task and provide appropriate rewards for participants is one of the main problems faced by MCS. This article proposes a reliable participant selection strategy driven by dynamic incentives. The whole participant selection process is divided into multiple stages. First, the platform calculates participants reliability by considering the task completion rate and task pass rate. Then, when the platform releases tasks at each stage, it considers the task deadline and the task completion progress to quantify the task requirements, and then determines the maximum price of the tasks at that stage. Finally, the platform selects participants who meet the requirements for different tasks at each stage and conducts bargaining games with participants to determine appropriate rewards. Considering the situation that participants quit midway and the task cannot be completed, the platform adopts a task delegation mechanism to ensure the effective completion of the task. The results show that the proposed strategy can guarantee the task completion rate and data quality, and optimize the reward of participants.
Transcriptome Analysis Identifies Tumor Immune Microenvironment Signaling Networks Supporting Metastatic Castration-Resistant Prostate Cancer

Prostate cancer (PCa) is the second most common cause of cancer death in American men. Metastatic castration-resistant prostate cancer (mCRPC) is the most lethal form of PCa and preferentially metastasizes to the bones through incompletely understood molecular mechanisms. Herein, we processed RNA sequencing data from patients with mCRPC (n = 60) and identified 14 gene clusters (modules) highly correlated with mCRPC bone metastasis. We used a novel combination of weighted gene co-expression network analysis (WGCNA) and upstream regulator and gene ontology analyses of clinically annotated transcriptomes to identify the genes. The cyan module (M14) had the strongest positive correlation (0.81, p = 4 × 10−15) with mCRPC bone metastasis. It was associated with two significant biological pathways through KEGG enrichment analysis (parathyroid hormone synthesis, secretion, and action and protein digestion and absorption). In particular, we identified 10 hub genes (ALPL, PHEX, RUNX2, ENPP1, PHOSPHO1, PTH1R, COL11A1, COL24A1, COL22A1, and COL13A1) using cytoHubba of Cytoscape. We also found high gene expression for collagen formation, degradation, absorption, cell-signaling peptides, and bone regulation processes through Gene Ontology (GO) enrichment analysis.
High-precision Indoor Visible Light Positioning with Tilt Receiver Based on Image Sensors

In recent years, visible light positioning (VLP) based on image sensors has been widely studied as the demand for indoor precise positioning services are increasing. We use commercial light emitting diodes (LEDs) as the transmitter and a commercial mobile phone or stereo camera as the receiver to acquire a fringe picture carrying information through the rolling shutter effect of the complementary metal oxide semiconductor (CMOS) image sensor. Based on this, the algorithm is proposed in this paper, either two dimensional positioning with the phone rotated at any angle in the horizontal plane, or further extension, three dimensional positioning with the stereo camera tilted at any rotation angle, can achieve good positioning effectiveness. Meanwhile, it also avoids the use of angle sensors that cannot provide reliable and accurate angle measurements continuously. The experimental results show that the system established in this paper is capable of achieving centimeter-level positioning accuracy.
Numerical simulation of a 2D electrothermal pump by lattice Boltzmann method on GPU

ABSTRACT Electrothermal flow in a microfluidic system is a fast-developing technology because of the advancement in micro-electro-mechanical systems. The motion is driven by the electrothermal force generated by the AC electric field and non-uniform temperature distribution inside the system. Electrothermal force can be explored for pumps in microfluidic systems. In this paper, the lattice Boltzmann method (LBM) is used to simulate a 2D electrothermal pump. As an alternative numerical method for fluid dynamics, LBM has many advantages compared with traditional CFD methods, such as its suitability for parallel computation. With its parallel characteristic, LBM is well fitted to the parallel hardware in graphic processor units (GPU). To save computational time in parametric studies, a CUDA code was developed for executing parallel computation. The comparison of computational time between CPU and GPU is presented to demonstrate the advantage of using GPU. The effects of the frequency, thermal boundary conditions, electrode size, and gap between electrodes on volumetric flow rate were investigated in this study. It was shown that LBM is an effective approach to studying 2D electrothermal pumps on a CUDA platform.
Self-Attention Guidance and Multiscale Feature Fusion-Based UAV Image Object Detection

Object detection on unmanned aerial vehicle (UAV) images is a recent research hotspot. Existing object detection methods have achieved good results on general scenes, but there are inherent challenges with UAV images. The detection accuracy of UAV images is limited by complex backgrounds, significant scale differences, and densely arranged small objects. To solve these problems, we propose a UAV image object detection network based on self-attention guidance and multiscale feature fusion (SGMFNet). First, we design a global-local feature guidance (GLFG) module. This module can effectively combine local information and global information, which makes the model focus on the object area and reduces the impact of complex background. Second, an improved parallel sampling feature fusion (PSFF) module is designed to efficiently fuse multiscale features. Third, we design an inverse-residual feature enhancement (IFE) module, which is embedded in the front of the newly added detection head to enhance feature extraction on small objects. Finally, we conduct a large number of experiments on the VisDrone2019 dataset. The results show that the proposed SGMFNet outperforms other popular methods and has achieved good results in many scenarios.
Development of a Novel Ball-and-Socket Flexible Manipulator for Minimally Invasive Flexible Surgery

This work proposes a novel flexible manipulator consisting of a series of 2-DOF vertebrae based on a ball-and-socket joint that is connected by a ball-shaped surface and a cup-shaped socket and constrained by pins for circumferential rotation. This manipulator can demonstrate outstanding torsional stiffness since the circumferential rotation between the vertebrae is constrained by four ball pins. The point contact between ball pins and guideways effectively reduces the friction between the vertebrae, thus allowing the designed manipulator to yield a smooth bending shape with constant curvature. This manipulator features high axial and torsional stiffness, excellent bending performance, sufficient loading capacity, and convenient integration with surgical instruments. Moreover, the excellent torsional stiffness enables this manipulator to efficiently transfer torque and be applied in in-situ torsional motion, effectively addressing the typical issue of limited dexterity for torsional motion. The kinematic modeling of the proposed manipulator under in-situ torsional motion has been derived, and its workspace has been analyzed. A robotic system has been assembled, and experiments have verified the proposed design and modeling validity. The results show that the maximum position errors in bending motion are 2.39% (horizontal direction) and 1.98% (vertical direction), and its torsional stiffness is 21.13N $\cdot $ mm/deg, which is 46 times higher than that of a typical spherical flexible manipulator (SFM). Such merits support this manipulator excellently performing the in-situ torsional motion with a maximum average position error of 3.58%. Furthermore, a phantom test of the larynx has been performed to verify the potential of clinical feasibility.
USE OF SIMPLIFIED THERMAL MODEL AND EARLY TIMES TO SOLVE INVERSE HEAT CONDUCTION PROBLEM

Abstract. Usually, thermal analysis in practical cases involves transient and three-dimensional models. In this case, numerical analysis or analytical solutions are needed to solve inverse heat conduction problem. The analytical solutions obtained in this case may have certain complexity, requiring a specific skill to implement and use. In some cases tridimensional problems can be reduced to bi or one dimensional without loss of precision. In others, one-dimensional problems with finite geometry can be reduced to simple geometry problems as the semi-infinite. The possibility of physical examination of these conditions may allow to obtain simplified solutions with the same confidence and precision in the most general and complex formulation. The study of the temperature for the early times can greatly simplify the thermal analysis and be sufficient to solve inverse heat conduction problema whithout loss of precision.
Modified Perturb and Observe Approach in MPPT for a Standalone Photovoltaic System

This paper proposes a modified perturb and observe algorithm approach to increase the output power of an independent photovoltaic system. Today, photovoltaic application as renewable energy power plant has been prevalent. This popularity is because photovoltaic power plants are easy to apply on-grid and off-grid schemes. In standalone power generation applications, the increased photovoltaic output power is of great help to users as it contributes to increasing overall system efficiency. The perturb and observe algorithm has been known as a reliable and inexpensive method. However, the performance still needs to be improved. Therefore, this study proposes a modified perturb and observe algorithm approach. The research results show the superiority of the proposed method.
Chromosome-scale genome assembly of Prunus pusilliflora provides novel insights into genome evolution, disease resistance, and dormancy release in Cerasus L.

Abstract Prunus pusilliflora is a wild cherry germplasm resource distributed mainly in Southwest China. Despite its ornamental and economic value, a high-quality assembled P. pusilliflora genome is unavailable, hindering our understanding of its genetic background, population diversity, and evolutionary processes. Here, we de novo assembled a chromosome-scale P. pusilliflora genome using Oxford Nanopore, Illumina, and chromosome conformation capture sequencing. The assembled genome size was 309.62 Mb, with 76 scaffolds anchored to eight pseudochromosomes. We predicted 33 035 protein-coding genes, functionally annotated 98.27% of them, and identified repetitive sequences covering 49.08% of the genome. We found that P. pusilliflora is closely related to Prunus serrulata and Prunus yedoensis, having diverged from them ~41.8 million years ago. A comparative genomic analysis revealed that P. pusilliflora has 643 expanded and 1128 contracted gene families. Furthermore, we found that P. pusilliflora is more resistant to Colletotrichum viniferum, Phytophthora capsici, and Pseudomonas syringae pv. tomato (Pst) DC3000 infections than cultivated Prunus avium. P. pusilliflora also has considerably more nucleotide-binding site-type resistance gene analogs than P. avium, which explains its stronger disease resistance. The cytochrome P450 and WRKY families of 263 and 61 proteins were divided into 42 and 8 subfamilies respectively in P. pusilliflora. Furthermore, 81 MADS-box genes were identified in P. pusilliflora, accompanying expansions of the SVP and AGL15 subfamilies and loss of the TM3 subfamily. Our assembly of a high-quality P. pusilliflora genome will be valuable for further research on cherries and molecular breeding.