Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel method for mechanical characteristics of actuators fast evaluation

In the era of Big Data, Cold data devours a large amount of storage volume and consumes power that cannot be ignored. Optical storage, as one of the cold data storage solutions, featuring with better unit bit cost, excellent reliability, and less power consumption, comes back to the spot of storge arena. To realize its potential, the actuators implementing high precision focusing and tracking in optical disk drives should be proved capable for high density recording. The traditional actuator performance testing is carried out by laser Doppler vibrometers (LDV), which not suitable for online inspection. Therefore, we propose a convenient and low-cost method for actuator dynamic performance testing for online inspection. By exciting coils and then analyzing certain signals from PDICs we can determine the dynamic characteristics of the actuator under testing, both in manufacturing and/or daily operating.
Link Prediction in Social Networks using Machine Learning

Prediction in social networks is an intriguing topic which has recently received a lot of attention. Given a snapshot of a dynamic social network, represented as a directed graph of nodes and edges (where nodes represent users and edges represent followers), the problem can be phrased as follows: - followed relations) Can we forecast which new relationships (links) will emerge in the near future? Such predictions can be used to recommend “friend suggestions” on Facebook, “fresh subjects” on Twitter, “interesting movies” and other tailored Netflix recommendations, among other things. The link prediction problem was proposed in this research for a dataset acquired from Twitch, an online video streaming platform.
REVIVING AND UPGRADING OF THE EP DEVICE

At Thomas Jefferson National Accelerator Facility, an electron beam is used to probe the fundamental properties of the nucleus. In these experiments, it is essential to know the precise energy of the beam. An important instrument along the beamline to measure the beam energy is the eP device. The device measures the scattered electron angle and the recoil proton angle of an elastic collision. From these angle measurements, the beam energy can be calculated. Many eP device components such as computer software, controls, and mechanical parts needed to be upgraded and/or replaced in order for the eP device to be operational again. A research study was conducted of the current hydrogen target and its properties as well as alternate targets for better performance. As the maximum electron beam energy incident on the eP device will soon be upgraded from 6 GeV to 12 GeV, an analysis was also done on potential changes to the position of the electron and proton detectors in order to accommodate this change. Calculations show that for the new energy upgrade, electron detectors need to be positioned at 5° above and below the beamline to measure the energy of 12 GeV. New proton detectors need tomore » be placed at an angle of 49.2° above and below the beamline to measure energies of 6.6 GeV and 8.8 GeV. With these changes the eP device will measure the range of new energies from 2.2 GeV to 12 GeV. From the target research studies it was found that a carbon nanotube mixture with polypropylene could be the ideal target for the eP device because of its high thermal conductivity and its high hydrogen content. The changes made to the eP device demonstrate the importance of continued research and new technologies. « less
Adoption of Information Technology and its Impact on Human Capital Performance in Micro and Small Enterprises in India

We examined the impact of adoption of information technology on human capital performance of MSEs in India. A sample of 500 respondent firms from north states of Punjab, Haryana and Himachal Pradesh has been collected from health care services and hospitality sector. To reach the target population judgmental sampling technique has been used. Multiple regression technique and descriptive statistics have been applied to analyse the results. The study found that information technology has positive impact on the work experience and overall employee productivity. So, it is concluded that investment in information technology significantly affects the human capital performance of firms. So, our findings recommended that micro and small firms should invest more and more in human capital to enhances entrepreneurial performance. It also helps in sustainability of business with optimum profits.
Lane Change–Intention Inference and Trajectory Prediction of Surrounding Vehicles on Highways

The behavior prediction of the surrounding vehicles is crucial when planning a minimal-risk path when realizing a collision-avoidance system. Herein, we propose a multiple model–based adaptive estimator (MMAE) that infers the lane-change intention of the surrounding vehicles and then predicts their trajectories. Specifically, first, a path is generated in the form of a cubic spline curve using the Frenet coordinate system, which is robust to changes in road curvatures. Linearized recursive least-squares estimation (LRLSE) method is used to adaptively predict a future trajectory based on the past trajectory of the target vehicle. Preview time is defined as a time-varying parameter that determines the final point of the path, and LRLSE updates it in real time. The MMAE applies LRLSEs to multiple paths and obtains the mode probability for each path, then the lane-change intention is inferred using the mode probability and preview time. The predicted future trajectory is the cubic spline curve determined based on the preview time. Further, we verify the performance of our approach using highD, a naturalistic dataset of vehicle trajectories, and compare it with those of existing methods. The proposed method does not require a large amount of data for training and has a low computational burden and high real-time performance.
The SmNPR4-SmTGA5 module regulates SA-mediated phenolic acid biosynthesis in Salvia miltiorrhiza hairy roots

Abstract Phenolic acids are the main bioactive compounds in Salvia miltiorrhiza, which can be increased by salicylic acid (SA) elicitation. However, the specific molecular mechanism remains unclear. The nonexpresser of PR genes 1 (NPR1) and its family members are essential components of the SA signaling pathway. Here, we report an NPR protein, SmNPR4, that showed strong expression in hairy root after SA treatment, acting as a negative moderator of SA-induced phenolic acid biosynthesis in S. miltiorrhiza (S. miltiorrhiza). Moreover, a basic leucine zipper family transcription factor SmTGA5 was identified and was found to interact with SmNPR4. SmTGA5 activates the expression of phenolic acid biosynthesis gene SmTAT1 through binding to the as-1 element. Finally, a series of biochemical assays and dual gene overexpression analysis demonstrated that the SmNPR4 significantly inhibited the function of SmTGA5, and SA can alleviate the inhibitory effect of SmNPR4 on SmTGA5. Overall, our results reveal the molecular mechanism of salicylic acid regulating phenolic acid biosynthesis in S. miltiorrhiza and provide new insights for SA signaling to regulate secondary metabolic biosynthesis.
Thinking of the Firm: How the Firm’s Benefit Influences Consumer Normative Expectations for Change

Abstract This research employs two scenario-based experiments to explore whether consumers “think of the firm” in setting their normative expectations regarding a customer-benefitting firm change. Results indicate that consumers take the firm benefit of potential firm actions into account in setting their expectations for what firms should do. Even in the absence of strong relationship ties, the associated firm benefit increases normative expectations for firms to enact a change. However, the customer benefit associated with a potential change dominates consumer expectations as it impacts normative expectations both directly as well as indirectly via an apparent lay belief that “what’s good for the customer is good for the firm. ” In addition, the customer and firm benefits of potential changes positively interact in driving expectations; when customers believe that a change will be a “win-win” for themselves and the firm they strongly expect the firm to enact it. The findings suggest that firms must make clear how the costs associated with a potential change will be borne by their customers.
Multi-agent Deep Reinforcement Learning for Multi-Cell Interference Mitigation

Multi-cell interference management techniques typically require sharing channel state information (CSI) among all cells involved, making the algorithms ineffective for practical uses. To overcome this shortcoming, an interference mitigation technique that does not require explicit CSI or coordination among neighboring cells is developed in this paper. The algorithm leverages distributed deep reinforcement learning to this end and delivers a faster and more spectrally-efficient solution than state-of-the-art centralized techniques. An important aspect of our proposed solution is that it scales very well with the number of cells in the network. The effectiveness of the proposed algorithm is verified by simulation over millimeter-wave networks with two to seven cells. Interestingly, the penalty for not sharing CSI decreases as the number of cells increases. In particular, for a 7-cell network, the proposed algorithm without sharing CSI achieves 92% of the spectral efficiency obtained by sharing CSI.
Coulomb potential learning

The Coulomb Potential Learning (CPL) algorithm (Bachmann et al., 1987), which derives its name from its functional form's likeness to a coulomb charge potential, was originally motivated by the short-comings of the Perceptron (Rosenblatt, 1962) and the original Hopfield net (Hopfield, 1979). In the case of the Perceptron, it was clear almost from the outset that the linear-separability provided by the perceptron would not be sufficient to perform complex tasks. In the case of the original Hopfield model, the recall capacity of the network is low due. to non-orthogonal memories and the existence of spurious memories. The CPL algorithm addresses both of these problems by providing a simple network which is capable of both storing an arbitrarily large number of memories with perfect recall and no spurious memories; and also constructing arbitrary nonlinear boundaries for classification tasks. In addition the CPL algorithm is easy to implement in hardware and is readily adaptable to parallel computation.
Improving Particle Thompson Sampling through Regenerative Particles

This paper proposes regenerative particle Thompson sampling (RPTS) as an improvement of particle Thompson sampling (PTS) for solving general stochastic bandit problems. PTS approximates Thompson sampling by replacing the continuous posterior distribution with a discrete distribution supported at a set of weighted static particles. PTS is flexible but may suffer from poor performance due to the tendency of the probability mass to concentrate on a small number of particles. RPTS exploits the particle weight dynamics of PTS and uses non-static particles: it deletes a particle if its probability mass gets sufficiently small and regenerates new particles in the vicinity of the surviving particles. Empirical evidence shows uniform improvement across a set of representative bandit problems without increasing the number of particles.
Wearable Antenna For Remote Health Monitoring

Remote health monitoring of elderly people is a serious issue of concern. Such an issue can be evaded by using wearable antennas which can get completely integrated onto clothes and use it for remotely transmitting/receiving the sensor data without affecting the elderly's regular habits. This research work aims to study and understand real wearability of the antenna and investigate the challenges in the design and fabrication of highly efficient antenna which can be fabricated on a fully flexible fabric substrate or a solid substrate. A wearable microstrip patch antenna with operating frequencies of 2.3 GHz (FR4 as substrate) and 2.66 GHz (Denim material as substrate) is designed and tested. Prominent materials considered for substrate parts are: Denim fabrics with the relative permittivity $\unicode{x20AC}\mathrm{r}=1.67$, and the loss tangent tan $\delta=0.085$ and FR4 (lossy) with the dielectric constant $\unicode{x20AC}\mathrm{r}=4.3$, and loss tangenttan $\delta=0.025$ material, while conventional copper metal sheets have been used as antenna conductive material. The antenna proposed is designed and simulated using CST simulator, fabricated using flexible copper sheet and tested. Results demonstrate that the designed antenna is very efficient, highly sensitive and of low-cost with performance meeting the expectation of the application in need. Further, proposed wearable antenna may also be attached to human body directly to sense suitable parameters.
A Vision-based System for Social Insect Tracking

Socia1 insects, especially honeybees, play an essential role in nature, and their recent decline threatens the stability of many ecosystems. The behaviour of social insect colonies is typically governed by a central individual, e.g., by the honeybee queen. The RoboRoyale project aims to use robots to interact with the queen to affect her behaviour and the entire colony’s activity. This paper presents a necessary component of such a robotic system, a method capable of real-time detection, localisation, and tracking of the honeybee queen inside a large colony. To overcome problems with occlusions and computational complexity, we propose to combine two vision-based methods for fiducial marker localisation and tracking. The experiments performed on the data captured from inside the beehives demonstrate that the resulting algorithm outperforms its predecessors in terms of detection precision, recall, and localisation accuracy. The achieved performance allowed us to integrate the method into a larger system capable of physically tracking a honeybee queen inside its colony. The ability to observe the queen in fine detail for prolonged periods of time already resulted in unique observations of queen-worker interactions. The knowledge will be crucial in designing a system capable of interacting with the honeybee queen and affecting her activity.
An ALOHA multi-user game with tradeoff between throughput and transmission proportional fairness

The paper considers an ALOHA type communication, where several users send data, and investigates the problem in a game-theoretic framework. We show that in such ALOHA type communication, the throughput metric leads to a continuum of equilibria. Moreover, its traditional extension via adding a transmission cost, which might collapse such a continuum to a finite number of equilibria, actually remains a multi equilibria situation, and that this might potentially destabilize users communication. Also we show that in contrast to the flat-fading multiple access communication (MAC) networks, in an ALOHA network, users' communication might not be stabilized via switching from throughput to latency communication utility. This phenomena puts forward a question of which metric might lead to a unique equilibrium, and thus to stability in multi user ALOHA type communication. Here for multi user ALOHA type communication we suggest an advanced communication metric reflecting tradeoff between throughput and the transmission's fairness. Such metric might be also interpreted as a trade-off for a user between its selfish and cooperative behavior. We prove that, in contrast to the throughput metric, such an advanced metric always lead to a unique equilibrium, and thus to communication stability under the corresponding transmission protocol. The other proven advantage is that it supports uninterrupted communication between the users. The equilibrium is derived in closed form. Finally, it is shown how to optimize weights of throughput and transmission's fairness in the suggested trade-off metric.
Passivity Enhancement-Based General Design of Capacitor Current Active Damping for LCL-Type Grid-Tied Inverter

Grid-side current control based on capacitor-current-feedback (CCF) active damping is a common control method for LCL-type grid-tied inverters. The control system should not only ensure inverter stability under complex grid conditions, but also be simple in structure. Therefore, the multiobjective design for the CCF control is proposed to enhance the passivity, while improving the internal stability. First, the passive constraint of CCF control is derived, from which two general control schemes, namely, negative CCF-based phase-lead (N-Lead) control scheme and positive CCF-based phase-lag (P-Lag) control scheme, are proposed. Then, the two schemes are implemented by the design of a first-order phase-lead CCF controller and a first-order phase-lag compensator-based positive CCF controller. Besides, the parameters design is also given for the convenience of engineering application. Finally, the experimental results verify the theoretical analysis and the effectiveness of the proposed design methods.
Surface Enhanced Infrared Absorption Spectroscopy with Broadband Nanoantenna

The infrared absorption spectra of analytes with many types of chemical bonds tend to have multiple absorption peaks in a broad band. Therefore, using the surface-enhanced infrared absorption (SEIRA) spectroscopic analysis system in an appropriate enhancing band would be helpful to improve its sensitivity. In this paper, the nanoantenna called six-pointed asterisk has been designed to have double resonance peak when irradiated by non-polarized light. The size of gold nanoantenna to obtain specific resonance peaks excited by different polarized light is designed based on linear wavelength scaling rule. At the same time, the arrangement period of nanoantenna array is selected by considering the collective resonance of nanoantenna array. The results of FDTD simulation are consistent with theoretical analysis. For the six-pointed asterisk array covering 1400-1000 cm-1 (7.14-10 μm) as the specific requirement, the EFs were calculated as follows, EFmin = EF7.14 μm ≈ 8.8×103, EF10 μm ≈ 1.2×104 and the EFmax ≈ 1.7×104. The designed six-pointed asterisk nanoantenna has the potential to customize a broad enhancing band in the SEIRA analysis system.
An Overview of Nanoemulgels for Bioavailability Enhancement in Inflammatory Conditions via Topical Delivery

The anti-inflammatory drugs that are generally available possess the disadvantage of hydrophobicity, which leads to poor permeability and erratic bioavailability. Nanoemulgels (NEGs) are novel drug delivery systems that aim to improve the solubility and permeability of drugs across the biological membrane. The nano-sized droplets in the nanoemulsion enhance the permeation of the formulation, along with surfactants and co-surfactants that act as permeation enhancers and can further improve permeability. The hydrogel component of NEG helps to increase the viscosity and spreadability of the formulation, making it ideal for topical application. Moreover, oils that have anti-inflammatory properties, such as eucalyptus oil, emu oil and clove oil, are used as oil phases in the preparation of the nanoemulsion, which shows a synergistic effect with active moiety and enhances its overall therapeutic profile. This leads to the creation of hydrophobic drugs that possess enhanced pharmacokinetic and pharmacodynamic properties, and simultaneously avoid systemic side effects in individuals with external inflammatory disorders. The nanoemulsion’s effective spreadability, ease of application, non-invasive administration, and subsequent ability to achieve patient compliance make it more suitable for topical application in the combat of many inflammatory disorders, such as dermatitis, psoriasis, rheumatoid arthritis, osteoarthritis and so on. Although the large-scale practical application of NEG is limited due to problems regarding its scalability and thermodynamic instability, which arise from the use of high-energy approaches during the production of the nanoemulsion, these can be resolved by the advancement of an alternative nanoemulsification technique. Considering the potential advantages and long-term benefits of NEGs, the authors of this paper have compiled a review that elaborates the potential significance of utilizing nanoemulgels in a topical delivery system for anti-inflammatory drugs.
Hemoglobin microbubbles and the prediction of different oxygen levels using RF data and deep learning

Ultrasound contrast agents (UCA) are gas-encapsulated microspheres that oscillate volumetrically when exposed to an ultrasound field producing backscattered signals efficiently, which can be used for improved ultrasound imaging and drug delivery applications. We developed a novel oxygen-sensitive hemoglobin-shell microbubble designed to acoustically detect blood oxygen levels. We hypothesize that structural change in hemoglobin caused due to varying oxygen levels in the body can lead to mechanical changes in the shell of the UCA. This can produce detectable changes in the acoustic response that can be used for measuring oxygen levels in the body. In this study, we have shown that oxygenated hemoglobin microbubbles can be differentiated from deoxygenated hemoglobin microbubbles using a 1D convolutional neural network using radiofrequency (RF) data. We were able to classify RF data from oxygenated and deoxygenated hemoglobin microbubbles into the two classes with a testing accuracy of 90.15%. The results suggest that oxygen content in hemoglobin affects the acoustical response and may be used for determining oxygen levels and thus could open many applications, including evaluating hypoxic regions in tumors and the brain, among other blood-oxygen-level-dependent imaging applications.
sEMG-Based Gesture Recognition Method for Coal Mine Inspection Manipulator Using Multistream CNN

With the rapid development of intelligent mining technology, remote-operated underground inspection and rescue robot have been widely used. This article recognized the operator’s emergency gestures based on forearm surface electromyography (sEMG). First, a wireless six-channel sEMG acquisition device is built and a dataset named coal mine inspection manipulator gestures (CMMG) is acquired; then, the features of each channel signal are extracted to a 2-D graph by a continue wavelet transform (CWT) method. The multistream convolutional neural network (CNN) model is built to analyze the feature graphs so as to detect action segments. The comparative experiments showed that the method improved the accuracy and showed better performance on both the CMMG dataset and the public Ninapro DB1 dataset.
Generalized regular variation of second order

Abstract Assume that for a measurable funcion f on (0, ∞) there exist a positive auxiliary function a(t) and some γ ∈ R such that . Then f is said to be of generalized regular variation. In order to control the asymptotic behaviour of certain estimators for distributions in extreme value theory we are led to study regular variation of second order, that is, we assume that exists non-trivially with a second auxiliary function a1(t). We study the possible limit functions in this limit relation (defining generalized regular variation of second order) and their domains of attraction. Furthermore we give the corresponding relation for the inverse function of a monotone f with the stated property. Finally, we present an Abel-Tauber theorem relating these functions and their Laplace transforms.