Miracle-dz/UniFAR-FTUs huggingface dataset:
Fixed‐time self‐structuring neural network cooperative tracking control of multi‐robot systems with actuator faults

In this study, a fixed‐time adaptive cooperative controller by a self‐structuring neural network is proposed, and actuator faults are considered for multi‐robot systems. First, a novel fixed‐time leader state observer is developed to estimate the state information of the leader and pass on to other followers without measuring the leader's velocity. Second, a fixed‐time cooperative controller is designed to achieve fast response and high precision. Third, a fixed‐time convergent self‐structuring neural network is designed to improve the approximation accuracy affected by system uncertainties and actuator faults. A new neuronal splitting strategy is designed to avoid excessive computational burden caused by too many neurons. Next, the Lyapunov stability theorem is employed to demonstrate that the whole error closed‐loop system can globally converge to a small region around zero in a fixed time. Finally, a simulation example on multi‐robot systems shows that the proposed fixed‐time adaptive cooperative controller is able to obtain satisfactory performances in the presence of uncertainties from external disturbances, actuator faults and other causes.
The complete mitochondrial genome of the Pale-legged Leaf-Warbler Phylloscopus tenellipes (Aves: Phylloscopidae) from Maor Mountain, China

Abstract The Pale-legged Leaf-Warbler (Phylloscopus tenellipes) (Swinhoe, 1860) is an olive-brown warbler that is extensively dispersed in Asia. We sequenced the whole mitochondrial genome of a Pale-legged Leaf-Warbler collected on Maor Mountain, Heilongjiang Province, China. The mitochondrial genome is 16,972 bp and contains 13 protein-coding genes, two rRNA genes, 22 tRNA genes, and one control region (CR). The CR is 1098 bp. The nucleotide sequence is composed of 29.11% A, 22.98% T, 14.64% G, and 33.27% C. Phylogenetic research revealed that P. tenellipes is closely related to Phylloscopus borealoides.
A Timestamp-Independent Haptic–Visual Synchronization Method for Haptic-Based Interaction System

The booming haptic data significantly improve the users’ immersion during multimedia interaction. As a result, the study of a Haptic-based Interaction System has attracted the attention of the multimedia community. To construct such a system, a challenging task is the synchronization of multiple sensorial signals that is critical to the user experience. Despite audio-visual synchronization efforts, there is still a lack of a haptic-aware multimedia synchronization model. In this work, we propose a timestamp-independent synchronization for haptic–visual signal transmission. First, we exploit the sequential correlations during delivery and playback of a haptic–visual communication system. Second, we develop a key sample extraction of haptic signals based on the force feedback characteristics and a key frame extraction of visual signals based on deep-object detection. Third, we combine the key samples and frames to synchronize the corresponding haptic–visual signals. Without timestamps in the signal flow, the proposed method is still effective and more robust in complicated network conditions. Subjective evaluation also shows a significant improvement of user experience with the proposed method.
On the Mixed Methods Designs in Language Education Research

With the broad application of mixed methods in social sciences, methodological designs in language education research has grabbed growing attention from scholars. This paper examines the use of mixed methods in empirical studies on language education from 2016 to 2020 in four International journals. Grounded on Creswell’s (2018) theory, the core design of the mixed method is further illustrated, expected to render some methodological enlightenment for future linguistics and education researchers.
Footstep Power Generation using Piezo Electric Sensors

The country's population is growing, and as a result, so is the demand for electricity. The amount of energy squandered is also rising in many different ways simultaneously. The primary approach is to regenerate this energy in a form that can be used. In this project, human footsteps are used to generate electricity, which is then used to recharge the battery. A battery that can be used to charge electronic gadgets stores the power. The Atmega 328 processor powers this system, which also includes an Arduino IDE, an RFID sensor, a USB cable, and an LCD. The system goes into registration mode when we turn it on. We are able to add three users. After each user has logged in, the system prompts them to swipe their cards and connect their chargers. Every user initially receives 5 minutes of charging time by default.
The productivity and cost of harvesting whole trees from early thinnings with a felling head designed for continuous cutting and accumulation

ABSTRACT The main problem with the utilization of untended stands for energy use is that small-diameter trees are expensive to harvest, and the value of the harvest fails to exceed the harvesting cost. Until now, the equation has appeared difficult to solve, but a wood harvesting innovation – the Risupeto II accumulating felling head – which works on a continuous basis, could provide a solution to the harvesting cost problem of young stands with a dense undergrowth. The novel accumulating felling head is attached to the boom of the crawler excavator, and trees are harvested at full-tree length. The crosscutting is done later during loading by a forwarder equipped with a grapple saw. The objectives of the present study were: 1) to define the productivity; 2) produce time consumption models for the Risupeto II harvesting unit in selective thinnings and 3) to determine the harvesting cost of whole trees from the early thinnings based on the above described two-machine configuration. Time study data from the 16 study plots consisted of 3,099 whole trees where the average tree volume varied between 14.2 and 52.0 dm3 resulting in 73 m3 of whole trees. The productivity varied between 11.2 and 26.6 m3/PMh on the time study plots indicating that felling-bunching productivity was relatively high compared to the latest studies with the Nordic harvesting technology in early thinning. According to the profitability analysis, the harvesting costs could be covered by the revenue from energy wood sales.
ANALYSIS OF FACTORS AFFECTING INTENTION TO USE E-WALLETS DURING THE COVID-19 PANDEMIC

Nowadays, the COVID-19 pandemic has greatly impacted human lives all around the world. One of the impacts that is quite noticeable is changes in lifestyle that must be adapted to the health protocols to reduce the spread of COVID-19. Along with the COVID-19 Pandemic, Indonesia also facing th era of e-payment, especially e-Wallet. E-Wallet is one of e-Payment which is server-based on the form of application that is used to store the user’s information. E-wallet can be used for e-commerce transaction and also as one of payment in offline store in Indonesia. They provide quick payment from the customer’s phone and also provide discount as their promotion. This study aims to analyze the influence of factors from the DeLone and McLean ISS Model which is user satisfaction, system quality and service quality, and other factors such as perceived COVID-19 risk, perceived usefulness, perceived risk, trust, economic benefit, government support, security risk, and financial risk towards the intention to use e-Wallets in offline stores during COVID-19. The data of this research were obtained from distributing questionnaires via google form to the e-Wallet users in Indonesia. By using the SEM-PLS method, research data from 300 respondents were analyzed with SmartPLS version 3.2. 8 software. The results showed that perceived COVID-19 risk, user satisfaction, and trust influences the intention to use e-Wallet in offline stores during the pandemic. In addition, the user satisfaction is also influenced by system quality and service quality. Meanwhile, the perceived usefulness and perceived risk did not affect the intention to use e-Wallet in offline stores during COVID-19.
Prediction of static strength properties of carbon fiber-reinforced composite using artificial neural network

In this paper, an artificial neural network (ANN) based model is developed considering the significant parameters affecting the strength properties of the fiber-reinforced composite. The model utilizes the experimental data obtained from Composite Materials Handbook, Volume 2—Polymer Matrix composites material properties (Military Handbook 17-1F). The data is extracted for unidirectional carbon fiber reinforced composite (CFRP) which represents the mean data obtained from experimentally tested specimens in batches. The dataset consists of 74 samples with eight input parameters: fiber strength, matrix strength, number of plies, loading axis, temperature, volume fraction, void percentage and thickness of ply. The output of the ANN model is the strength of the composite. The hyper-parameter of the ANN model is tuned and selected optimally. The network architecture arrived at is 8-[4]-1 with training function as Levenberg–Marquardt and activation function as tan-sigmoid in the hidden layer and pure-linear in the output layer. The agreement between the prediction from the developed model and experimental data is satisfactory, indicating the model’s applicability and efficacy. The trend analysis with respect to the input parameters is also carried out to verify that the model captures the mechanics-based behavior of CFRP.
Research on power modeling of the industrial robot based on ResNet

With the development of intelligent manufacturing and the popularization of industrial robots (IR), the energy consumption (EC) of IR become increasingly significant and attract attention from academia and industry. Accurate EC modeling and prediction is the premise and basis for energy efficiency optimization of IR. In recent years, intelligent algorithms like neural networks have been introduced into the EC modeling of IR, and high-precision models can be constructed without complex modeling and parameter identification process. In contrast with the direct modeling method based on mechanism model, data-driven modeling has significant efficiency and application advantages. In this paper, we propose a ResNet-based IR power modeling method. The deep learning algorithm is used to establish the precise mapping between the operating parameters and power of the industrial robot. Then, the model is applied to another industrial robot by transfer learning, which realizes the rapid construction and deployment of power models for different IR. The experimental results show that the proposed IR power modeling method can accurately construct the power model and achieve 21.83% less error. After transfer learning, the power model can be accurately constructed even when the amount of data is reduced by 80%, which facilitates the large-scale practical application of IR and their energy efficiency improvement.
An 18.1 mW 50 MHz-BW 76.4 dB-SNDR CTSDM With PVT-Robust VCO Quantizer and Latency-Free Background-Calibrated DAC

This paper presents a continuous-time sigma-delta modulator (CTSDM) with a voltage-controlled-oscillator-based (VCO-based) integrating quantizer. A background replica-based calibration technique is proposed to alleviate the impact of the process, voltage supply, and temperature (PVT) variations on the tuning characteristic and current consumption of the VCO-based quantizer. Matching between the replica VCO and the main VCO in the proposed calibration is not needed. A latency-free background calibration technique is also proposed to eliminate the distortions caused by the DAC mismatch. The prototype VCO-based CTSDM is fabricated in a 40 nm CMOS and achieves SNDR/SFDR/DR of 76.4 dB/91.7 dBc/79.6 dB, respectively, within a 50 MHz bandwidth (BW) at 1.6 GHz sampling frequency. The measured SNDR varies within ±1 dB over a temperature range of $0\sim 80~^{\circ }\text{C}$ and a voltage supply variation of ±10%, across different tested samples. The power consumption is 18.1 mW, achieving a Schreier Figure of Merit (FoMS) of 170.8 dB.
Complex Network Analysis of Mass Violation, Specifically Mass Killing

News reports in media contain news about society’s social and political conditions. With the help of publicly available digital datasets of events, it is possible to study a complex network of mass violations, i.e., Mass Killings. Multiple approaches have been applied to bring essential insights into the events and involved actors. Power law distribution behavior finds in the tail of actor mention, co-actor mention, and actor degree tells us about the dominant behavior of influential actors that grows their network with time. The United States, France, Israel, and a few other countries have been identified as major players in the propagation of Mass Killing throughout the past 20 years. It is demonstrated that targeting the removal of influential actors may stop the spreading of such conflicting events and help policymakers and organizations. This paper aims to identify and formulate the conflicts with the actor’s perspective at a global level for a period of time. This process is a generalization to be applied to any level of news, i.e., it is not restricted to only the global level.
An Efficient Transfer and Ensemble Learning Based Computer Aided Breast Abnormality Diagnosis System

Breast cancer is the second most deadly type of cancer globally among women and can be prevented to a great extent in the case of early detection. In order to raise the survival rate, research scientists have conducted several experiments to develop tools to alleviate this problem, including Computer-Aided Diagnosis (CADx) systems. Deep Learning and its important sub-field Convolutional Neural Networks (CNN)s have revolutionized (CADx) development research. While the Curated Breast Imaging Subset of Digital Database for Screening Mammography, or the CBIS-DDSM dataset, has been classified using different pre-trained architectures, few of them have used ensemble learning to provide a more robust and accurate architecture. To the best of our knowledge, we are the first to integrate the application of the state-of-the-art pre-trained model called EfficientNet along with other pre-trained models for the part, and subsequently, the models were concatenated (ensembled). With the application of pre-trained CNN-based models, we are able to address the problem of not having a large dataset. Nevertheless, with the EfficientNet family offering better results with fewer parameters, we obtained significant improvement in accuracy, and later ensemble learning was applied to provide robustness for the network. After performing 10-fold cross-validation, our experiments yielded promising test accuracy results, 96.05% and 85.71% for abnormality type and pathology diagnosis classification, respectively.
EFECTO DE ACARICIDAS QUÍMICOS EN LA MORTALIDAD DE LA ARAÑA ROJA Tetranychus urticae KOCH (Acari: Tetranychidae)

Background. Chemical acaricides with different modes of action are an effective alternative for the management of T. urticae and to prevent the selection of resistant population. Objective. To evaluate the effect of chemical acaricides of different toxicological groups on the mortality of T. urticae. Methodology. To evaluate the acute toxicity, mites were exposed to leaf discs of eggplants that had been immersed in the acaricides. The mortality was recorded at 4 h period for a total of 48 h. To evaluate the residual effect, the acaricides were applied to eggplant plants, leaf discs were obtained at different periods (1 - 32 days after acaricide application) and T. urticae adults and nymphs were exposed to the leaf discs. Results. Abamectin, spirodiclofen and fenpyroximate had the fastest effects in adults and nymphs (4 h after application). Abamectin, spirodiclofen and fenpyroximate had long-term effects, for up to two weeks. Spirodiclofen, fenpyroximate and acequinocyl had also ovicidal effect. Implication. Chemical acaricides with different mode of action represent a viable option for the management of T. urticae and the prevention of the selection of resistant populations. Conclusión. Abamectin, spirodiclofen, and fenpyroximate are highly toxic against adults and immature T. urticae.
Co-infection of mammarenaviruses in a wild mouse, Tanzania

Abstract Mammarenaviruses are bi-segmented RNA viruses. They encompass viruses responsible for several severe diseases in humans. While performing a de novo assembly of a new virus found in a wild single-striped grass mouse in Tanzania, we found a single S but two divergent L segments. Natural co-infections, common within reptarenaviruses in captivity, were never reported for mammarenaviruses and never in a wild sample. This finding can have implications for virus evolution as co-infection could trigger viral recombination/reassortment in natural reservoirs.
Morphological and Genetic Characterization of Eggerthella lenta Bacteriophage PMBT5

Eggerthella lenta is a common member of the human gut microbiome. We here describe the isolation and characterization of a putative virulent bacteriophage having E. lenta as host. The double-layer agar method for isolating phages was adapted to anaerobic conditions for isolating bacteriophage PMBT5 from sewage on a strictly anaerobic E. lenta strain of intestinal origin. For this, anaerobically grown E. lenta cells were concentrated by centrifugation and used for a 24 h phage enrichment step. Subsequently, this suspension was added to anaerobically prepared top (soft) agar in Hungate tubes and further used in the double-layer agar method. Based on morphological characteristics observed by transmission electron microscopy, phage PMBT5 could be assigned to the Siphoviridae phage family. It showed an isometric head with a flexible, noncontractile tail and a distinct single 45 nm tail fiber under the baseplate. Genome sequencing and assembly resulted in one contig of 30,930 bp and a mol% GC content of 51.3, consisting of 44 predicted protein-encoding genes. Phage-related proteins could be largely identified based on their amino acid sequence, and a comparison with metagenomes in the human virome database showed that the phage genome exhibits similarity to two distantly related phages.
Wideband Sparse Spectrum Sensing With the Virtual Grid Model

Spectrum sensing is an effective method to improve spectrum utilization. In this paper, we propose a wideband spectrum sensing scheme based on the virtual grid model. Specifically, we firstly divide the network coverage area into grids with a certain accuracy according to the virtual grid model, and virtualize user nodes. And we only consider the impact of these virtual nodes on receiving nodes, while ignoring the actual transmitting nodes. Secondly, we transform the wideband spectrum sensing into a multi-dimensional joint compressive sensing problem according to the sparsity in the wideband frequency domain and the correlation in space domain. Finally, the sensing matrix is properly designed by minimizing the maximum column coherence in order to get better recovery accuracy, and the compressive sensing-based algorithm is proposed. Simulation results show that the proposed algorithm can effectively improve the performance of wideband spectrum sensing.
Efficient Assessment and Large-Scale Conservation of Intra-Varietal Diversity of Ancient Grapevine Varieties: Case Study Portugal

There are thousands of ancient grapevine varieties in Europe, each one having a high level of intra-varietal diversity with regard to important economic traits (yield, soluble solids content, acidity, anthocyanins, and others). However, this potential has become exposed to a process of genetic erosion since the middle of the last century. The main objective of this work is to present experimental strategies for conservation and utilization of intra-varietal diversity. A concrete example is given about the actions performed in Portugal since 1978. Two main approaches for the conservation of intra-varietal diversity were performed: (1) strict conservation (in pots and in the field without experimental design) for future generations; and (2) conservation and, simultaneously, evaluation of the intra-varietal variability for selection to fulfil the immediate needs of the grape and wine sector (in the field with experimental design). More than 30,000 accessions of Portuguese autochthonous varieties are conserved. Using the theory of mixed models, intra-varietal diversity of the yield was found for the 59 varieties studied. The conservation and the evaluation of the intra-varietal diversity for quantitative traits will allow to extract high economic value, as well as to ensure its utilization to meet the objectives of the vine and wine sector.
Extended envelopes around Galactic Cepheids V. Multi-wavelength and time-dependent analysis of IR excess (cid:63)

Aims. We aim to investigate the infrared excess of 45 Milky Way (MW) Cepheids combining di ﬀ erent observables in order to constrain the presence of circumstellar envelopes (CSEs). Methods. We used the SpectroPhoto-Interferometry of Pulsating Stars (SPIPS) algorithm, a robust implementation of the parallax- of-pulsation method that combines photometry, angular diameter, stellar e ﬀ ective temperature, and radial velocity measurements in a global modelling of the pulsation of the Cepheid. We obtained new photometric measurements at mid-infrared (mid-IR) with the VISIR instrument at the Very Large Telescope complemented with data gathered from the literature. We then compared the mean magnitude of the Cepheids from 0.5 µ m to 70 µ m with stellar atmosphere models to infer the IR excess, which we attribute to the presence of a circumstellar envelope. Results. We report that at least 29% of the Cepheids of our sample have a detected IR excess ( > 3 σ ). We estimated a mean excess of 0 . 08 ± 0 . 04mag at 2.2 µ m and 0 . 13 ± 0 . 06mag at 10 µ m. Other Cepheids possibly also have IR excess, but they were rejected due to their low detection level compared to a single-star model. We do not see any correlation between the IR excess and the pulsation period as previously suspected for MW Cepheids, but a rather constant trend at a given wavelength. We also do not ﬁnd any correlation between the CO absorption and the presence of a CSE, but rather with the stellar e ﬀ ective temperature, which conﬁrms that the CO features previously reported are mostly photospheric. No bias caused by the presence of the circumstellar material is detected on the average distance estimates from a SPIPS analysis with a ﬁtted colour excess. We also do not ﬁnd correlation between the presence of IR excess and the evolution stage of the Cepheids. Conclusions. We report a fraction of 29% of Cepheids with an IR excess likely produced by the circumstellar envelope surrounding the stars. Longer period Cepheids do not exhibit greater excess than short periods as previously suspected from observations and theoretical dusty-wind models. Other mechanisms such as free-free emission, among others, may be at the origin of the formation of the CSEs. We also show that not ﬁtting the colour excess leads to a bias on the distance estimates in our Galaxy.