Miracle-dz/UniFAR-FTUs huggingface dataset:
Micropropagation of Mountain Mulberry (Morus bombycis Koidz.) ‘Kenmochi’ on Cytokinin-Free Medium

The aim of the study was to compare two methods of micropropagation of mulberry: single-node culture (“SNC”), and axillary-branching (“AxB”). The experiments were carried out on in vitro cultures for 6 successive passages. The “AxB” cultures were propagated on modified MS medium (+ 25% Ca2+ and Mg2+), supplemented with WPM vitamins, sucrose (30 g L−1), and BA (1.5 mg l-1). The “SNC” cultures were grown on cytokinin-free 1/2 MS (macro- and micronutrients) medium supplemented with WPM vitamins, IBA (0.05 mg l-1), and sucrose (15 g l-1). Both media (pH 5.8) were solidified with agar (7.0 g l-1). Initiation of in vitro cultures from explants taken from adult trees and young, potted plants was feasible on both media. Cultures were established from about 1 cm long nodal explants. Generally “SNC” cultures formed one well rooted, significantly longer axillary shoot with bigger leaves than “AxB” cultures, which developed significantly more shoots and big callus at the explant base. All shoots collected from “SNC” and “AxB” cultures rooted in vivo in peat mixture and developed into similar plantlets. The single-node method based on application of cytokinin-free medium is a good alternative for the axillary-branching method for micropropagation of mountain mulberry (Morus bombycis) ‘Kenmochi”’.
A Programmatic Approach for Development of the ViewHRV Service Platform with Accurate and Reliable Results

There are dozens of available packages and libraries that claim to calculate HRV. This paper aims at comparing the results from the calculation of a single array of intervals between normal beats, including the most popular open source Python HRV measurement packages available on GitHub. Furthermore, the same array was ran through the Kubios software and compared to the previous results. In order to compare the accuracy of the results, as a benchmark we used the C programs provided by Physionet. The results showed a huge difference in the results with almost all the indices, in fact the simplest measurement that of Standard Deviation of NN Intervals showed to be incorrect in Kubios and in most of the Python packages. Results like these are the reason we decided to develop our own package to calculate HRV. Finally, the goal of this paper is to present details on developing a publicly available web service platform ViewHRV with guaranteed precision obtaining accurate and reliable results.
Combining One-vs-One Decomposition and Instance-Based Learning for Multi-Class Classification

Multi-class classification is one of the most important supervised learning problems and can be solved by either designing direct multi-class classifiers (direct strategy) or decomposing it into a set of binary classification problems (indirect strategy). Direct strategy only needs training one unified classifier while indirect strategy, especially the one-vs-one decomposition method, has shown its superiority and been utilized by some popular software packages. In this article, a first attempt towards bridging the gap between direct strategy and one-vs-one decomposition for multi-class classification is conducted, and accordingly a novel approach named CODIL is proposed. Specifically, CODIL firstly transforms the class vector into a ternary label matrix (only with <inline-formula> <tex-math notation="LaTeX">$\{-1,0,+1\}$ </tex-math></inline-formula>) via one-vs-one rule, where each column of the label matrix corresponds to a pair of classes. Then, CODIL determines the binary label vector (only with <inline-formula> <tex-math notation="LaTeX">$\{-1,+1\}$ </tex-math></inline-formula>) for unseen instance by exploiting the manifold structure information residing in its <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest neighbors, where each element of the label vector denotes the unseen instance’s prediction for its corresponding class pair. Finally, the class for unseen instance is returned via majority voting based on the binary label vector. Extensive comparative studies are conducted between CODIL and six well-established multi-class approaches over seventeen benchmark multi-class data sets. The experimental results show the superiority of the proposed CODIL approach against the compared approaches.
Design and Application of a Photovoltaic-Energy Storage Joint System with Active Frequency Regulation Capability

With the high proportion of photovoltaic power generation replacing traditional energy generation, the frequency regulation capability of the power system is weakened. How to improve the frequency regulation capability of the power system where distributed photovoltaic is densely accessed is an important factor to promote the consumption of new energy. To this end, this paper firstly proposes a structure of a photovoltaic combined energy storage unit to form a joint photovoltaic-energy storage system(PV-ES). Under the premise of not increasing the grid-connected inverter, the energy storage unit and the DC side of the photovoltaic inverter are controlled for power exchange, the frequency support is quickly provided when the power grid needs, and the state of charge(SOC) of the energy storage unit is managed. Then, the actual photovoltaic power station is selected and the active frequency control system is established. Finally, the effectiveness of the proposed scheme is verified by monitoring the charge and discharge of the energy storage unit under frequency disturbance.
IOT Based Automatic Vehicle Accident Alert System

The concept of detecting car accidents is not fresh and the automobile industries have made tremendous progress in optimizing the technology. This paper is an try and make a contribution in that location of generation. Here we are seeking to stumble on accident through the Accelerometer as it facilitates in identifying the vicinity and if the values of x, y and z parameters are extra than the defined values than it ’s going to set situation to proper and the code written for initiating the intimation and SMS alert gets executed. With this method the accident location can be detected easily and the information of the accident location can be sent via the GPS to the emergency offerings for assistance.
Trust Aspects of Internet of Things in the Context of 5G and Beyond

The Sixth-generation communication system (6G) relies on some groundbreaking technological enablers to provide IoT with its full realization. However, with a myriad of user equipment and IoT devices connected everywhere with different levels of capability, 6G will need a holistic approach to reconcile the strict security requirements with any Ultra-Reliable Low-Latency Communication (URLLC) among smart objects. As a key part of the 6G development, Blockchain is considered as an essential technology to simultaneously address both these stringent demands. In this paper, we consider a two-level approach, where trust is modeled by combining a high-level component based on Blockchain and Identity-Based Encryption (IBE) to manage device identities, and a low-level component based on a temporal decay and relative entropy components. Numerical results obtained from two different datasets substantiate our trust approach as a promising option aiming to shed some light on how the trust aspects should be considered in the context of 6G.
ANFIS Controller Design Using PSO Algorithm for MPPT of Solar PV System Powered Brushless DC Motor Based Wire Feeder Unit

In this study an improved control scheme for autonomous photovoltaic (PV) generator fed wire feeder unit (WFU) of arc welding machine using a brushless DC (BLDC) motor is developed. As power generated from PV energy systems always depends on varying weather conditions, to get maximum output power from PV generator at every insolation variation, Adaptive Neuro Fuzzy Inference System (ANFIS) based MPPT controller is combined with particle swarm optimization (PSO) algorithm to track the MPP of the PV generator. The suggested hybrid technique identifies and tracks the MPP and provides accurate and fast response of an autonomous PV wire feeder unit (PV-WFU). To maintain the welding wire speed constant under varying conditions, a fractional-order PI (FOPI) speed controller based on hysteresis current control (HCC) scheme is adopted to drive the BLDC motor of WFU. A MATLAB/SIMULINK model is developed for this purpose and results are presented. The simulations show that the proposed hybrid PSO-ANFIS based on MPPT controller has been able to control the PV output power around the maximum point at all weather condition scenarios. In addition, the proposed FOPI controller successfully tracked the welding wire speed for WFU over traditional PI controller.
Implementation of Backtracking Algorithm for navigation in LoRa-enabled wearable device for Dementia patients

Dementia is a condition which results in a continuous decline in thinking, behavioural and social skills that disrupts a person’s ability to function independently. One major predicament every dementia patient has to endure revolves around him/her getting stranded in the middle of the road, not even knowing how to track his/her way to their residence. The paper emphasises the role of an all-in-one device which will monitor the patient’s location, show them the path back home, alert their caretaker or family members in an emergency situation and remind them of their daily tasks. The proprietary algorithm developed, the hardware and software architecture implemented has been discussed in detail. The system developed is an all-in-one solution which is intuitive and cost-effective, straightforward to use and requires minimal energy (long battery life).
Computation Offloading for Vehicular Environments: A Survey

With significant advances in communication and computing, modern day vehicles are becoming increasingly intelligent. This gives them the ability to contribute to safer roads and passenger comfort through network devices, cameras, sensors, and computational storage and processing capabilities. However, to run new and popular applications, and to enable vehicles operating autonomously requires massive computational resources. Computational resources available with the current day vehicles are not sufficient to process all these demands. In this situation, other vehicles, edge servers, and servers in remote data centers can help the vehicles by lending their computing resources. However, to take advantage of these computing resources, computation offloading techniques have to be leveraged to transfer tasks or entire applications to run on other devices. Such computation offloading can lead to improved performance and Quality of Service (QoS) for applications and for the network. However, computation offloading in a highly dynamic environment such as vehicular networks is a major challenge. Therefore, this survey aims to review and organize the computation offloading literature in vehicular environments. In addition, we demystify some concepts, propose a taxonomy with the most important aspects and classify most works in the area according to each category. We also present the main tools, scenarios, subjects, strategies, objectives, etc., used in the works. Finally, we present the main challenges and future directions to guide future research in this active research area.
Multi-sensor Data Fusion Algorithm Based on Adaptive Trust Estimation and Neural Network

Multi-sensor data fusion technique plays a key role in the agricultural services such as data collection and processing. However, the collected data usually is featured by redundancies and errors, which deteriorate the reliability of network. In this paper, based on adaptive trust estimation, we propose a multisensor data fusion algorithm in Trust Neural Network (T-NN), aiming to solve the problem of low accuracy and poor stability of multi-sensor data fusion. In particular, the original data collected by the sensors first are pre-processed by exponential smoothing. Then, trust estimation model is applied to calculate the value of trust among the sensing nodes and optimize the data, and the performance of redundancy and reliability are enhanced. Furthermore, the data optimized is introduced into BP neural network for training and fusion. Extensive simulations show that the algorithm proposed in this paper greatly outperforms adaptive weighted average model and traditional BPNN model, in terms of the accuracy of data fusion.
Frequency-Adaptive Neural Network Based Virtual Flux Estimation for Sensorless Control of PWM Converters Under Unbalanced Conditions

In this paper, sensorless control of three-phase pulse-width modulation converters under unbalanced grid conditions is proposed. This method exploits a new virtual flux (VF) estimator based on a frequency-adaptive neural network (FANN) configured as a quadrature signal generator (QSG). The inherent frequency-adaptive characteristic of the FANN-QSG is obtained using new developed frequency-locked loop. Two parallel FANNQSGs are utilized for simultaneous VF estimation and symmetrical components sequence separation in the proposed FANN-VF estimator. This approach avoids cascaded delays related to cascaded VF estimation and sequence separation structures. The proposed FANN-VF estimator has been tested as part of VF-based predictive current control (VF-PCC) system. Simulation results show that the FANN-VF estimator presented fast and accurate estimation of the grid frequency and the positive and negative sequence VF components. Moreover, the obtained VF-PCC achieved better current waveforms, under unbalanced conditions, than those obtained by the conventional PCC which uses grid voltage measurement.
Synthèse et formulation des nanoparticules polymère ciblant l'E-sélectine : évaluation in vitro dans un modèle d'endothélium activé

Ce travail de these avait pour objectif d’elaborer un systeme vecteur ciblantl’endothelium pathologiquement active dans les tissus enflammes, infectes ou tumoraux.Ce vecteur est sous forme de nanoparticules decorees de ligands glycosides capablesd’interagir avec l’E-selectine, un recepteur exprime sur les cellules endothelialesactivees.Nous avons mis au point une synthese de copolymere amphiphile avec une architecturea bloc muni sur sa partie hydrophile d’un ligand glucidique. Ce copolymere a ete par lasuite utilise pour la preparation de nanoparticules de type coeur/couronne. La partiehydrophobe centrale est entouree d’une couronne hydrophile dont l'encombrementsterique et la mobilite limitent l'opsonisation de la particule. Le ciblage actif a ete assurepar la presence d’un ligand du recepteur de l’E-selectine aux extremites des chaines depolymeres hydrophiles a la surface du vecteur.Avec ces nanoparticules dont les proprietes de surface sont predefinies, nous avonsmontre in vitro l’association efficace avec les cellules endotheliales activees, ce qui apermis de valider ce concept de ciblage moleculaire actif par l’intermediaire du couplerecepteur/ ligand. Un tel systeme permettra d’ameliorer l’indice therapeutique et labiodistribution des principes actifs anti-inflammatoire et/ou anticancereux.
Evaluating Smartphone Addiction Disorder among University Students

Smartphones have become essentials in our life in this era, especially in the life of university students. It has become a one-stop-centre for many things. Students set schedules and meetings on their phone, set up online meetings via skype, make conference video calls to communicate with their friends and family, review their course notes on the phone as well as participate in online classes with a smartphone. The usage of smartphones has become endless. Together with that, addiction towards smartphones has become inevitable. Smartphone addiction has caused many issues, such as emotional distress, neck pain and sleep disorder. In this paper, we report the results of a quantitative study that was conducted to evaluate smartphone addiction disorder among 135 university students. A survey consisting of the 26 items of Smartphone Addiction Inventory (SPAI) was administered to determine the impact of smartphone usage on university students and to study the factors related to the addiction. Exploratory Factor Analysis (EFA) was carried out to study the underlying connection within the 26 items and the students. Results indicate that smartphone addiction among the university students is in the average, as the total SPAI score for 73 participants focuses in the middle range. The EFA outcome suggested a two-factor solution: Dependency and Well-beingness. The results of our study indicate that students' wellbeing is getting affected by the usage of smartphones.
Feature Extraction and SVM Parameters Optimization Based on ICQPSO

In order to further study the fault diagnosis of analog circuit and improve the effectiveness of the application of SVM parameter optimization method, a fault diagnosis method of analog circuit based on ICQPSO feature extraction and SVM parameter optimization is proposed by combining the feature processing method with SVM parameter optimization from the perspective of feature extraction. In order to improve the classification accuracy and generalization ability of SVM, ICQPSO is used to optimize the transformation matrix and SVM parameters. The simulation results show that this method is very effective for the construction of high-precision SVM, and it is feasible to apply it to the fault diagnosis of analog circuits.
System Setup for Synchronized Visual-Inertial Localization and Mapping

A novel approach for synchronization and calibration of a camera and an inertial measurement unit (IMU) in the research-oriented visual-inertial mapping-and localization-framework maplab is presented. Mapping and localization are based on detecting different features in the environment. In addition to the possibility of creating single-case maps, the included algorithms allow merging maps to increase mapping accuracy and obtain large-scale maps. Furthermore, the algorithms can be used to optimize the collected data. The preliminary results show that after appropriate calibration and synchronization maplab can be used efficiently for mapping, especially in rooms and small building environments.
Categorizing Volcanic Seismic Events with Unsupervised Learning

We explored three different clustering-based classifiers to categorize two different volcanic seismic events and to find possible overlapping signals that could occur at the same time or immediately after seismic events occurrence. The BFR classifier with k=2 was chosen as the best out of 27 explored models statistically (p$\lt$0.05), reaching a mean of accuracy score of 88%. This result represents a satisfactory and competitive classification performance when compared to the state of art methods. The CURE classifier with k=3 attained a mean of accuracy value of 87% at p$\lt$0.05, allowing it to be the only model capable of detecting seismic events with overlapping signals. Therefore, the proposed clustering-based exploration was effective in providing competitive models for seismic events classification and overlapped signal detection.
18F-Fluorocholine Positron Emission Tomography/Computed Tomography is a Highly Sensitive but Poorly Specific Tool for Identifying Malignancy in Thyroid Nodules with Indeterminate Cytology: The Chocolate Study

Background: Refining the risk of malignancy in patients presenting with thyroid nodules with indeterminate cytology (IC) is a critical challenge. We investigated the performances of 18F-fluorocholine (FCH) positron emission tomography/computed tomography (PET/CT) to predict malignancy. Methods: Between May 2016 and March 2019, 107 patients presenting with a thyroid nodule ≥15 mm with IC and eligible for surgery were included in this prospective study. Head-and-neck PET/CT acquisitions were performed 20 and 60 minutes after injection of 1.5 MBq/kg of FCH. PET/CT acquisition was scored positive when maximal standardized uptake value in the IC nodule was higher than in the thyroid background. Pathology was the gold standard for diagnosis. Results: At pathology, 19 (18%) nodules were malignant, 87 were benign, and one was a noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Sensitivity, specificity, accuracy, positive-predictive value (PPV), and negative-predictive value (NPV) of FCH PET/CT in detecting cancer or NIFTP were 90%, 50%, 55%, 29%, and 96% at 20 minutes and 85%, 49%, 67%, 28%, and 94% at 60 minutes, respectively. Higher specificity (58% vs. 33%, p = 0.01) was observed in nononcocytic (n = 72) than in oncocytic IC nodules (n = 35). The pre-PET/CT probability of cancer or NIFTP in Bethesda III–IV nodules was 11% and the post-PET/CT probability was 19% in PET-positives and 0% in PET-negatives. In retrospective analysis, 42% of surgeries would have been unnecessary after PET/CT and 81% before (p < 0.001), resulting in a hypothetical 48% reduction (95% confidence interval [32–64]). Conclusions: FCH PET/CT offers high NPV to reliably exclude cancer in PET-negative IC nodules, but suffers from low PPV, particularly in those with oncocytic cytology. ClinicalTrials.gov identifier: NCT02784223.
Correlation based fault management for centralized protection and control

Fault management of the distribution network is today predominantly based on single point measurements, which are processed in bay level protection and control relays. This paradigm is now changing, due to the introduction on the market of centralized protection and control platforms, which are capable of receiving multiple separate digital measurement streams. This paper presents a new analysis method for substation measurements, where station level correlation coefficients are used for fault management. The proposed method can be used both for post-mortem analysis, as well as for real-time protection. The method has been tested and verified with real live data, gathered over a period of 1.5 years from a pilot substation utilizing a centralized protection and control solution. The main benefits of the proposed methods are simplification of the engineering process via a reduction of individual function instances and faster operation due to the availability of sub-cycle signal processing. In addition, a new digital finger-print for network events is proposed, that can be further utilized in other data driven applications.
Pre-synchronization Control of Grid-connected Three-phase Inverters Based On the Characteristics of Synchronous Generators

A pre-synchronization control strategy for grid-connected three-phase inverters is proposed in this paper ,to solve the problem of slow response of the conventional synchronous unit containing a phase-locked loop (PLL), based on the synchronization principle between the synchronous generator (SG) terminal voltage and the grid voltage when there is no power exchanged. In this paper, the voltage control loop and the frequency control loop is analyzed in detail. The influence of the phase tracking principle and parameters of the controller on transient responses of the system is fully discussed. The simulation and experimental results show that the improved three-phase inverter can track the grid voltage rapidly, accurately, and steadily, by using this control scheme without the PLL.
Synthesis of a Broadband Ring Antenna of a Two-Tape Design

Proposed options the EMA on the basis of two-ring structures with end slots. They feature a design that contains two rings located one in one. To perform the fractal transformation, the arc acts as the initiator and as the generator acts "meander". Offered multiple templates of the generator allow you to create single or double-sided quasi-fractal 3D-structure. The range of models expanded variation of the mutual angular rotation templates. To simplify the synthesis of antennas vector description of multidimensional fractal transformation. For the analysis of spatial frequency characteristics of the designed antenna solutions used indicators such as return loss, beam pattern і voltage standing wave ratio. The estimates obtained fractional bandwidth δf = 1.22 … 1.32 indicate the prospects of the proposed approach for the realization of antenna systems with high bandwidth supports.