Miracle-dz/UniFAR-FTUs huggingface dataset:
Remaining Useful Life Prediction via Multi-Neural Network Integration

The remaining useful life prediction is an important components of the prognostic and health management system. The traditional remaining useful life prediction for the bearing cannot consider the spatial-temporal features of the degraded signal, and depends the manual extraction of the bearing degra-dation features. The single neural network model is difficult to maintain the best feature extraction in different degradation stages of bearings. In this paper, the remaining useful life prediction framework is developed via the multi-neural network integration. The different neural network can effectively extract the different features of the original signal, adaptive weight integration algorithm is proposed to integrate the multi-neural network. The bearing degradation data is used to verify the effectiveness of the developed framework, which is collected on the PRONOSTIA platform. The experimental results show that the developed framework can effectively reduce the average percentage error for the remaining useful life prediction of bearing.
Multi agent consensus control based on improved sliding mode controller

This paper discusses the consensus of the second-order multi-agent pilot follower system. The absolute value term is used to improve the convergence speed of the sliding mode controller and suppress chattering when approaching the sliding mode surface. The improved first-order sliding mode controller and the improved boundary layer-based sliding mode controller are designed respectively and the stability analysis is carried out respectively. Its effectiveness is verified by MATLAB simulation.
Wireless Capsule Endoscope Localization with Phase Detection Algorithm and Adaptive Body Model

Wireless capsule endoscopes take and send photos of the human digestive tract, which are used for medical diagnosis. The capsule’s location enables exact identification of the regions with lesions. This can be carried out by analyzing the parameters of the electromagnetic wave received from the capsule. Because the human body is a complex heterogeneous environment that impacts the propagation of wireless signals, determining the distance between the transmitter and the receiver based on the received power level is challenging. An enhanced approach of identifying the location of endoscope capsules using a wireless signal phase detection algorithm is presented in this paper. For each capsule position, this technique uses adaptive estimation of human body model permittivity. This approach was tested using computer simulations in Remcom XFdtd software using a numerical, heterogeneous human body model, as well as measurements with physical phantom. The type of transmitting antenna employed in the capsule also has a significant impact on the suggested localization method’s accuracy. As a result, the helical antenna, which is smaller than the dipole, was chosen as the signal’s source. For both the numerical and physical phantom studies, the proposed technique with adaptive body model enhances localization accuracy by roughly 30%.
Distributed Finite-Time Secondary Control of DC Microgrids Based on Virtual Capacitance

The distributed finite-time secondary control for the DC microgrid is introduced in this paper. When the microgrid's operating state changes, the finite-time secondary controller can accelerate the DGs' output voltage and current convergence speed and improve the system's rapidity. Virtual capacitors are added to distributed secondary control to increase the DC microgrid's inertia. The simulation example verifies that the finite-time secondary controller can effectively improve the response speed of the microgrid and proves that virtual capacitors can effectively enhance the anti-interference ability of the microgrid.
Real-time Walking Speed Estimation by Involving Phase Variables based on Deep Learning

Lower limb prothesis is neccessary in daily avtivities for unilateral transfemoral amputees and gait analysis plays an important role in user-friendly knee-ankle prothesis research. Walking speed is recognized as an important feature that influences joint trajectories, the estimation of which is vitual to guarantee walking fluency and symmetry. In this paper, walking speed is estimated in real-time by involving phase variable and radius as the inputs of deep learning network. Virtual Constraint control algorithm is used to generate prosthetic joints trajectories as functions of phase variable through the entire gait by collecting thigh angle from an Inerial Measurement Unit (IMU) sensor mounted on the thigh. Besides, Long Short-Term Memory (LSTM) network is applied to train the estimation model. In consideration of the tight relationship between walking speed and phase plane curves, this research utilizes horizontal and vertical coordinates, variable and phase radius as the inputs of deep learning network instead of directly using raw sensors data. The simulation experiments proved that this algorithm could estimate the speed effectively, and be more accuate than using raw sensors data.
A Position Control Algorithm of Bucket Wheel Reclaimer Based on LADRC

Bucket wheel reclaimer (BWR) is broadly used in the process of reclaiming operation in mines and metallurgical enterprises. The Accuracy and dynamic response performance of of its position control algorithm directly affects the efficiency of unmanned equipment. In this paper, a position control algorithm of BWR based on linear active disturbance rejection control (LADRC) is proposed, and a extended state observer (ESO) is designed to observe the total disturbance of the system caused by the uncertain friction due to high dust and muck as well as the parameter uncertainty related to different material quality of the loader. Besides, the stability of the observer is analyzed theoretically. Simulation shows that the algorithm can effectively achieve the control objectives, dynamic performance and steady-state error can be well considered, besides, the algorithm has strong anti-disturbance ability.
A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects

In the present era, social media platforms such as Facebook, WhatsApp, Twitter, and Telegram are significant sources of information distribution, and people believe it without knowing their origin and genuineness. Social media has fascinated people worldwide in spreading fake news due to its easy availability, cost-effectiveness, and ease of information sharing. Fake news can be generated to mislead the community for personal or commercial gains. It can also be used for other personal benefits such as defaming eminent personalities, amendment of government policies, etc. Thus, to mitigate the awful consequences of fake news, several research types have been conducted for its detection with high accuracy to prevent its fatal outcome. Motivated by the aforementioned concerns, we present a comprehensive survey of the existing fake news identification techniques in this paper. Then, we select Machine Learning (ML) models such as Long-Short Term Memory (LSTM), Passive Aggressive Algorithm, Random Forest (RF), and Naive Bayes (NB) and train them to detect fake news articles on the self-aggregated dataset. Later, we implemented these models by hyper tuning various parameters such as smoothing, drop out factor, and batch size, which has shown promising results in accuracy and other evaluation metrics such as F1-score, recall, precision, and Area under the ROC Curve (AUC) score. The model is trained on 6335 news articles, with LSTM showing the highest accuracy of 92.34% in predicting fake news and NB were showing the highest recall. Based on these results, we propose a hybrid fake news detection technique using NB and LSTM. At last, challenges and open issues along with future research directions are discussed to facilitate the research in this domain further.
Design of Force Control Parameters Considering Cycle Time

This paper presents a new approach to the design of force control parameters for robotic assembly. Appropriate force control parameters are necessary in order to achieve assembly operations successfully and assure high efficiency of the operations. In practical assembly lines, a cycle time that is the time to perform an operation is required to be reduced. In this paper, a designing method of force control parameters that can reduce a cycle time is proposed and applied to Peg-in-Hole operations. In the method, sub-optimal parameters are obtained through iterative simulations of assembly operations because it is very difficult to calculate a cycle time analytically. First, the proposed method is formulated as an optimization problem. Next, a simulator for robotic Peg-in-Hole operations is developed based on preliminary experiments. Force control parameters are then optimized by using the simulator. Finally, experimental results are shown to demonstrate the validity of the parameters obtained through the proposed method.
Semi-passive Walking Control of Biped Robot with Muscle Model

In this paper, the plane stable walking control problem of a semi-passive biped robot with a muscle model was studied. The spring in the Spring-loaded inverted pendulum (SLIP) model was replaced by the modified Hill muscle model, and the dynamic equations of the biped robot in the single support and double support stages were established by the Lagrange method. Human-like control strategies include a combination of passive and active contraction of the leg muscles to keep the total energy of the system near a certain value and controlling the height and forward velocity of the particle by adjusting the landing position of the swinging leg. Based on theoretical analysis, simulation was carried out in the MATLAB environment. The results show that the control method proposed in this paper can realize the stable periodic walking of the biped robot based on the muscle model. When the system is adjusted with external disturbance, the total energy can be restored to balance and the robot can re-enter the stable periodic walking. Moreover, compared with the SLIP model, the proposed control method has stronger robustness.
A Single Short ‘Tone Burst’ Results in Optimal Drug Delivery to Tumours Using Ultrasound-Triggered Therapeutic Microbubbles

Advanced drug delivery systems, such as ultrasound-mediated drug delivery, show great promise for increasing the therapeutic index. Improvements in delivery by altering the ultrasound parameters have been studied heavily in vitro but relatively little in vivo. Here, the same therapeutic microbubble and tumour type are used to determine whether altering ultrasound parameters can improve drug delivery. Liposomes were loaded with SN38 and attached via avidin: biotin linkages to microbubbles. The whole structure was targeted to the tumour vasculature by the addition of anti-vascular endothelial growth factor receptor 2 antibodies. Tumour drug delivery and metabolism were quantified in SW480 xenografts after application of an ultrasound trigger to the tumour region. Increasing the trigger duration from 5 s to 2 min or increasing the number of 5 s triggers did not improve drug delivery, nor did changing to a chirp trigger designed to stimulate a greater proportion of the microbubble population, although this did show that the short tone trigger resulted in greater release of free SN38. Examination of ultrasound triggers in vivo to improve drug delivery is justified as there are multiple mechanisms at play that may not allow direct translation from in vitro findings. In this setting, a short tone burst gives the best ultrasound parameters for tumoural drug delivery.
Psychological Impact of COVID-19 Patient Care on Families of Healthcare Workers

Objective: The Coronavirus‑19 (COVID‑19) pandemic has perpetrated a significant mental health burden amongst health care workers (HCWs) and their families. Families of HCWs are at significant risk of various mental health problems due to concerns regarding the wellbeing of the HCW, fear of contacting the virus from HCWs, staying isolated during periods of quarantine and being the passive recipient of public stigma directed towards HCWs. The objective of this study was to assess the perceived stress, resilience and coping tendencies of families of HCWs managing the COVID 19 crisis. Method : A cross sectional study was conducted at a tertiary care hospital in India, where close relatives of 150 HCWs managing COVID-19 patients (directly or indirectly involved in patient care) were selected using systematic random sampling. They were evaluated using the Perceived Stress Scale, Brief Resilience Coping Scale, and the Hospital Anxiety and Depression Scale. Pearson’s correlation and Independent t-test were used for statistical analysis using the SPSS software. Results: Majority (75%) of the family members associated with frontline HCWs had moderate to high level of perceived stress, while23% and 17% had clinically significant anxiety and depressive symptoms, respectively. More than 50% had low resilience and coping scores. High levels of stress, anxiety and depression were seen in female respondents, those less than 40 years old, having a child or an elderly family member and when the HCW had direct involvement in COVID-19 patient care. Conclusion: Families of HCWs suffer from significant psychological burden due to the COVID-19 crisis. They face significant risk of depression and anxiety associated problems, which are augmented by low resilience and impaired coping. Mental health of HCWs and their family members should be given due attention with a focus on early identification, providing psychological support and improving resilience.
Apatite U-Pb Dating and Composition Constraints for Magmatic–Hydrothermal Evolution in the Giant Renli Nb-Ta Deposit, South China

Apatite is a nearly ubiquitous accessory phase in igneous rocks that crystallizes during the entire magma evolution process and has great implications for geochronology and petrogenesis. Previous studies suggested that Nb-Ta mineralization in the giant Renli deposit was genetically related to Late Jurassic two-mica monzogranite or Early Cretaceous muscovite monzogranite. Moreover, the magmatic–hydrothermal evolution of these two stages is poorly understood. In our study, we confirm that the muscovite monzogranite, biotite monzogranite, and two-mica monzogranite are all spatially associated with Nb-Ta pegmatites. We present new apatite U-Pb ages to constrain the timing of Nb-Ta mineralization and related magmatism. The results show that apatite from the two-mica pegmatite yield a lower intercept age of 130 ± 2 Ma (2σ), and apatite grains from two two-mica pegmatite samples yield a lower intercept age of 135 ± 8 Ma (2σ) and 134 ± 3 Ma (2σ), respectively. Apatite and whole-rock geochemistry suggest the oxidation degree of the Nb-Ta mineralization increases from north (RL-6) to south (RL-16) in the giant Renli deposit. This study demonstrates that a combination of apatite composition and U-Pb ages can be used to constrain the magmatic–hydrothermal evolution of granite and pegmatite-type Nb-Ta deposits.
LSTM-Based Prediction Model for Tool Face State of Sliding Directional Drilling

For sliding directional drilling, because of the lack of quantitative control experience and the difficulty of physical modeling, the control parameters of the torsional pendulum system are artificially chosen according to the motion state of the tool surface, which leads to the effect of directional control depends on the artificial experience, thus the control efficiency is uneven, the problem of human resources cost is large and the efficiency is low. Aiming at more effective controlling of torsion pendulum in sliding directional operation, an intelligent prediction model for tool face state based on Long-Short Term Memory (LSTM) network is proposed to make more effective decisions for control parameters. Experiments show that the temporal sequence and nonlinear law in sliding directional operation can be well learned. Moreover, the effectiveness of the proposed prediction model is tested on several real oil drilling platforms. The research results have great potential applications in intelligent and automatic directional drilling process.
Deep embedding for mental health content on social media using vector space model with feature clusters

Over the years, various document‐clustering techniques were developed to group the textual data. The performance of document clustering systems heavily relies on the optimal use of text representations. Vector space model is an extensively used technique by existing clustering algorithms to present the text in a structured form. However, such representations suffer from a lack of semantic associations, high dimensionality, and sparsity. In order to enrich the document representation by retaining the semantic and morphological associations, this paper introduced a word cluster‐based modified term frequency‐inverse document frequency (WC_MTI) model, in which semantically associated word embeddings from the word2vec are supplemented with morphological information using kernel principal component analysis. In addition, to address high dimensionality and sparsity issues and improve the clustering, we use a self‐training technique that learns discriminative features using the WC_MTI model and autoencoder (AE) and then updates the encoder network weights using assignments from a clustering algorithm as supervision. The proposed model organizes documents into topically compatible clusters by maintaining the semantic and morphological similarity between terms using the skip‐gram with negative sampling (SGNS) and low‐dimensional vector representations. We evaluate the proposed approach against the existing text representation methods. Experimental findings suggest that the proposed approach enhanced the average accuracy by 89.62%.
Validating a TPACK instrument for 7–12 mathematics in-service middle and high school teachers in the United States

Abstract This study fills a gap with TPACK instrumentation by validating a survey instrument for use specifically with secondary mathematics in-service teachers in the United States using an instrument originally developed in Australia (Handal et al., 2012). A comparable national sample was surveyed in the U.S. to the original Australian instrumentation study. Findings revealed the factor structure of the Australian TPACK instrument differed when used in the U.S. and presents a new validated instrument (TPACK-M-US) for use with secondary mathematics in-service teachers in the U.S. We provide three sources of validity evidence (e.g. Instrument or Test Content, Internal Structure, Response Processes). Appropriate uses and interpretations are discussed in addition to the importance of validation research for educational settings.
Fluorescent Polymers Conspectus

The development of luminescent materials is critical to humankind. The Nobel Prizes awarded in 2008 and 2010 for research on the development of green fluorescent proteins and super-resolved fluorescence imaging are proof of this (2014). Fluorescent probes, smart polymer machines, fluorescent chemosensors, fluorescence molecular thermometers, fluorescent imaging, drug delivery carriers, and other applications make fluorescent polymers (FPs) exciting materials. Two major branches can be distinguished in the field: (1) macromolecules with fluorophores in their structure and (2) aggregation-induced emission (AIE) FPs. In the first, the polymer (which may be conjugated) contains a fluorophore, conferring photoluminescent properties to the final material, offering tunable structures, robust mechanical properties, and low detection limits in sensing applications when compared to small-molecule or inorganic luminescent materials. In the latter, AIE FPs use a novel mode of fluorescence dependent on the aggregation state. AIE FP intra- and intermolecular interactions confer synergistic effects, improving their properties and performance over small molecules aggregation-induced, emission-based fluorescent materials (AIEgens). Despite their outstanding advantages (over classic polymers) of high emission efficiency, signal amplification, good processability, and multiple functionalization, AIE polymers have received less attention. This review examines some of the most significant advances in the broad field of FPs over the last six years, concluding with a general outlook and discussion of future challenges to promote advancements in these promising materials that can serve as a springboard for future innovation in the field.
Distributed Optimization for Economic Dispatch Over Time-Varying Directed Networks With Row-Stochastic Matrices

In recent years, with the continuous development of power grid, the power system is also transitioning to a more intelligent power grid. However, the economic dispatching problem of power system needs effective coordination in order to achieve greater benefits. In this paper, a distributed projection gradient algorithm based on row-stochastic over time-varying directed graph is proposed to solve the economic dispatch problem of power system. The algorithm uses row-stochastic condition instead of double-stochastic condition and column-stochastic condition, so that each intelligent power system can independently assign the weight value of neighboring nodes according to the situation, which improves the autonomous controllability of the system. The convergence of the algorithm is analyzed theoretically, and it is proved that the algorithm can match the optimal solution. Finally, the validity and accuracy of the algorithm are verified by numerical experiments.
Risk Analysis for Tunnel Rockburst Based on Fracture Mechanics and Stochastic FEM

Stress-strength ratio model is usually applied to rockburst prediction in China and abroad,but uncertainties in engineering are not taken into account in this method.A fracture mechanics model of rockburst mechanism is proposed in this paper.This model is based on the assumption that rockburst is caused by microcracks which parallel to the free surface.These microcracks will fracture,extend,and mutually connect under external load.Then visible macroscopic crack is formed and finally rockburst happens.Furthermore,a rockburst risk probability model is proposed using reliability theory and stochastic FEM method based on Lamination spallation buckling model.The model was applied to engineering project calculation and the result manifested its rationality.
Biological Activity of Pulcherrimin from the Meschnikowia pulcherrima Clade

Pulcherrimin is a secondary metabolite of yeasts belonging to the Metschnikowia pulcherrima clade, and pulcherrimin formation is responsible for the antimicrobial action of its producers. Understanding the environmental function of this metabolite can provide insight into various microbial interactions and enables the efficient development of new effective bioproducts and methods. In this study, we evaluated the antimicrobial and antiadhesive action of yeast pulcherrimin, as well as its protective properties under selected stressful conditions. Classical microbiological plate methods, microscopy, and physico-chemical testing were used. The results show that pure pulcherrimin does not have antimicrobial properties, but its unique hydrophilic nature may hinder the adhesion of hydrophilic bacterial cells to abiotic surfaces. Pulcherrimin also proved to be a good cell protectant against UV–C radiation at both high and low temperatures.