Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of organic materials as amendments in the remediation of soils contaminated by lead, copper and zinc

The effectiveness of using amendments in metal stabilization was determined, and their potential for remediation of contaminated soil was evaluated. A combined approach of evaluating soil and plant metal availability as well as microbial respiration was used as an indication of effectiveness of metal immobilization. This was done by sorption studies, incubation experiments in contaminated media, and greenhouse pot experiments. Batch sorption studies were conducted to evaluate the ability of bone meal, composts, peat, coir and wood bark to sorb Pb, Cu and Zn. Single sorption carried out over a metal concentration range of 0.1 mmol 1-1 using 0.001M and 0.1M Ca(NO3)2 as background electrolytes showed very high sorption of Pb, Cu and Zn by the amendments, but high background salt led to a reduction in the amount of metal sorbed by all amendments. Of the amendments tested, coir, compost and wood bark were most effective. Sorption was evaluated by applying the Langmuir equation, and maximum sorption values were calculated for all amendments. Theoretical maximum sorption of Pb was 87 mg g-1 (0.42 mmol g-1) by coir and green waste compost; Cu was 30 mg g-1 (0.47 mmol g-1) by green waste and general compost, while maximum Zn sorption was 14 mg g-1 (0.21 mmol g-1) in composts, closely followed by 13 mg g-1 (0.19 mmol g-1) in coir. Sorption mixed metal solutions of Pb, Cu and Zn was evaluated in a background salt of 0.001M Ca(NO3)2 only, either at equimolar concentrations of 0.1 mmol 1-1, equimolar concentrations of 1 mmol 1-1, or combinations of metals at either concentration. Metal sorption was reduced in the presence of other metals when compared with sorption in single metal solution. Pb sorption in equimolar solution of 0.1 mmol 1-1 was approximately 50% of that in single solution, Cu was 35%, while Zn was 40% if wood bark was not considered (wood bark sorption of Zn in low equimolar metal was not different from that in single Zn solution). The effect of metals on microbial respiration was evaluated in metal spiked amendments over a ten week period using metal solutions of Pb, Cu and Zn as contaminants. Pb, Cu and Zn toxicity led to an inhibition in CO2 evolved in all amendments, as addition of any amount of Pb, Cu or Zn led to a decrease I amount of CO2 evolved when compared with the non-contaminated amendments. The effect of increasing metal toxicity on CO2 evolution was reflected best in coir, where inhibition increased with an increase I metal concentration. In other contaminated amendments however, the inhibition was highly pronounced once there was metal contamination regardless of the metal concentration.
Mining User’s Opinions and Emojis For Reputation Generation Using Deep Learning

Millions of people express their opinions and viewpoints about products and services directly online. Twitter is one of the popular platforms used by internet users. This massive amount of people’s opinions is very relevant for automatically computing and drawing a reputation score. Therefore, having a system capable of generating a reputation value from reviews shared by the users will be a beneficial tool for companies to assess the reputation of their outputs and make favorable changes. However, a limited number of studies have inquired mining opinions expressed in natural language for reputation generation. Therefore, our approach consists of a system that generates a reputation by applying the deep learning Bidirectional Encoder Representations from Transformers (BERT) as an embedding layer and using a multi-layer Gated recurrent units (GRU) which learns from the representations produced by the transformer. Also, processing the emojis included in the opinions with a simple technique for a reliable reputation value. Experimental results conducted on two twitter datasets about different products show that our system provides the nearest reputation value to the ground truth, which is weighted average vote for each of our products provided by (IMDB and Yelp). This implies that the proposed approach can be applied in real world applications.
Active Sampling Rate Calibration Method for Acoustic Sensor Networks

The sampling rate mismatches among nodes severely degrade the performances of distributed signal processing methods in acoustic sensor networks. An active sampling rate calibration method based on the multiple signal classification (MUSIC) algorithm is proposed to solve this problem. Specifically, a pre-defined sinusoidal signal is used as the calibration sound source, and the sampling rate mismatch problem is formulated based on the Hadamard product between output signals of pairwise nodes. The Hadamard product is then filtered through the pre-designed filters to obtain the target sinusoidal signals whose frequencies linearly correlate with the sampling rate mismatch. Finally, the sampling rate differences among nodes are estimated by the root-MUSIC algorithm and compensated by the sinc-interpolation. The proposed method can effectively calibrate the sampling rate mismatch even under severe noise and reverberation. Moreover, it has a strong tolerance for deviations among the microphone frequency responses. Experimental results reveal the validity of the proposed method.
A Method for ACM on Q/V-Band Satellite Links Based on Artificial Intelligence

This paper compares classical algorithms for adaptive coding and modulation (ACM) with an approach based on artificial intelligence (AI). The proposed approach utilizes an online random regression forest (ORRF) to predict time series of signal to noise ratio (SNR) values aiding the ACM switching decisions. The evaluation of the ACM algorithms is based on two years of Q/V-band channel data recorded at the ground station in Graz using the Alphasat experimental Q/V-band payload. The results indicate that the ORRF based approach could outperform the classical approaches in terms of spectral efficiency, and parameterization of the ORRF is simpler and needs less knowledge of the channel properties as the discussed classical ACM approaches.
Distributed Optimization Design of Multi-Agent Systems With Packet Losses

In this paper, we investigate a distributed optimization problem of the continuous-time multi-agent systems (MASs) with packet losses. We first prove the boundedness of the state in the presence of packet loss, which means that the gradients of objective functions are not required to be bounded. On this basis, we propose a sampled-data distributed dynamic scheme such that the all agents’ states can converge in probability to an exact optimal point. Furthermore, if we know the probability of random packet losses in advance, the upper bound of the sampling interval is derived. An illustrative example is given which substantiate the usefulness of the proposed method.
Event-triggered Control Strategies for Rendezvous Problem of Multiple Linear Systems

This paper studies the rendezvous problem of linear multi-agent systems by event-triggered strategies based on piecewise constant communication signals of neighboring agents. There are two distinguished features of the design. First, the distributed event-triggered control laws can not only accomplish tasks of asymptotic tracking and external disturbance rejection as existing event-triggered consensus control strategies, but also have the additional ability of maintaining the connectivity of the time-varying and position-dependent communication network as existing rendezvous controllers. Second, by combining potential function technique, output regulation theory and adaptive control technique, an event-triggered observer is applied to estimate both the leader’s system matrix and signal, which can work in parallel with the distributed event-triggered control, i.e., the executive time instants for the observer and the control are asynchronous and generated by different triggering functions based on their own locally available measurement errors.
Clustering-Based Penalty Signal Design for Flexibility Utilization

As the penetration level of renewable energy sources (RES) increases, the associated technical challenges in the power systems rise. Enhancing the utilization of energy flexibility is known to be the main key to overcome the load-supply balance challenge caused by RES. In this regard, the trend is toward the utilization of demand-side flexibility. Meanwhile, individual penalty signals positively affect the utilization of available flexibility from the demand-side. Previous studies in this field are based on designing penalty signals according to electricity price and regardless of the demand situation. However, designing and implementing a proper penalty signal with finite amplitude requires analyzing large datasets of load, storage and generation. Therefore, to fill this gap in designing a proper penalty signal we have proposed a novel approach in which, clustering is used to overcome the complexity of analyzing large datasets. The main goal of the proposed method is to utilize energy flexibility from responsive batteries according to a request from the aggregator without violating the consumers’ privacy and comfort level. Therefore, aggregator’s attainable load and generation datasets are used in the case studies to maintain the practicality of the proposed method. Simulation results show the proposed penalty signal designing method effectively increases the available flexibility of microgrids.
Backward Difference Sampled-Data First-Order Linear Active Disturbance Rejection Control

This paper investigates how to obtain sampled-data first-order linear active disturbance rejection control (FOLADRC) when the sampling period is given and not small enough. Backward difference approach is suggested, its formulas are given, and it is compared with Euler’s method. Theoretical analysis based on root locus and numerical examples show backward difference is better than Euler’s method when the sampling period is large. And a criterion is given to judge if Euler’s method is suitable to use.
Validation of Results of Brno CHF Experimental Loop in Annular Channel

This study presents an experimental investigation of critical heat flux on Inconel 625 and compares the results with experimental work as well as correlations from literature. Tests were performed on the loop at pressure up to 300 kPa and mass flux rate up to 0.4 kg/(m2·s). The heated tube was 380 mm long with an outside and inside diameter of 9.14 mm and 8 mm respectively. The boiling crisis was achieved using DC heating and the temperature of test section was measured using K-type thermocouples. Finally, recorded values were compared to and validated with the analytical correlations from scientific literature and other experimental results.
A Robust 5 MW Split-Pole Reluctance Synchronous Wind Generator

In this paper the consequences for the performance of a reluctance synchronous wind generator, when removing some complexity from its rotor, are considered. The investigation is done on a 5 MW power level with the generator in the medium speed range. Complex flux barrier shapes are avoided in the designs. As a result, a reluctance generator with a simple salient pole rotor is optimized in its design and used as a baseline machine. Skewing and a fractional slot winding is implemented to address a high torque ripple. Finally, a suggested split-pole design is shown to increase the efficiency of the simple reluctance generator to above 98% with a torque ripple below 5% and a power factor of around 0.65.
New Fault-Location Algorithm for Series-Compensated Double-Circuit Transmission Line

This paper proposes a new fault location algorithm to accurately locate the faults of series-compensated double-circuit transmission line and improve the stability of power system operation, which is not affected by fault types. The proposed algorithm is based on asynchronous voltage and current at both ends of the line, the pre-fault voltage and current are used to synchronize time. The six-sequence component transformation is introduced, and with the help of the phase-sequence selection rule, the proposed algorithm eliminates the non-linear impedance of metal oxide varistor (MOV). Simultaneously, to confirm the robustness of the algorithm, different fault types, different fault initial angles and different fault resistances are taken into account in the system simulation. Not only that, the influence of series capacitors (SCs) installation positions, line parameter errors, different sampling rates and measurement errors are also tested. In general, the proposed fault-location algorithm has excellent performance. PSCAD/EMTDC and MATLAB are used in simulation studies, the results show that the maximum estimated error of fault-location does not exceed 1.8631%.
Deep Learning in Skin Disease Image Recognition: A Review

The application of deep learning methods to diagnose diseases has become a new research topic in the medical field. In the field of medicine, skin disease is one of the most common diseases, and its visual representation is more prominent compared with the other types of diseases. Accordingly, the use of deep learning methods for skin disease image recognition is of great significance and has attracted the attention of researchers. In this study, we review 45 research efforts on the identification of skin disease by using deep learning technology since 2016. We analyze these studies from the aspects of disease type, data set, data processing technology, data augmentation technology, model for skin disease image recognition, deep learning framework, evaluation indicators, and model performance. Moreover, we summarize the traditional and machine learning-based skin disease diagnosis and treatment methods. We also analyze the current progress in this field and predict four directions that may become the research topic in the future. Our results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect.
Impact of Synchronous Generator Replacement with VSG on Power System Stability

The stability of frequency and voltage endure high risk due to the integration of power converters. One of the promising solution is the virtual synchronous generator, which controls the power converter to mimic the characteristics of a synchronous generator. This paper evaluates the impact of using the virtual synchronous generator instead of an actual synchronous generator on the power system. Two different modeling methods are considered, where a high order model is used to investigate that how close a virtual synchronous generator can emulate a synchronous generator, and a low order model is used to investigate the influence when only the key features of the synchronous generator are modeled, such as the inertia. The IEEE 9-bus modified test system is used to obtain the simulation results in this paper. It shows that a 6th-order model based VSG is detailed enough to emulate a synchronous generator in most cases and a 2nd-order model based VSG behaves differently, which may introduce both advantages and disadvantages.
District Performance of the ATMs by Alternative DEA Techniques

The performance evaluation of the service industry has been always a challenging topic in the literature. In real life, these evaluations are generated in general by third party companies basing on financial ratios. Automated Teller Machines (ATMs) are the 7/24 customer contact point of the banks and their performance directly affects the banks overall performance. In this study, the ATM performances of a Turkish bank, located in a specific zone are evaluated with alternative Data Envelopment Analysis (DEA) models. The results of the case study are further evaluated by the experts and would be beneficial to approach ATM deployment problem.
Electric Vehicle Charging Loads in Residential Areas of Apartment Houses

Electric vehicle (EV) charging is expected to constitute a substantial part of the power demand in residential areas in the future. Its effects on the principles of distribution network planning and load profiles need to be studied. In this paper, we evaluate the feasibility of replacing the conventional cars with EVs in Finnish district heated apartment house areas. Stochastic simulation is used to combine a probabilistic charging model and electricity consumption data. We estimate the effects of uncontrolled, curtailed and price-optimized residential EV charging with different charging powers defined in the standards. The simulation results show that with 11.1 kW and 22.2 kW uncontrolled charging, the example apartment house connections would be overloaded. It is also found that the price-optimized charging should include a coordinated control logic that supports the local grid. With 1.84 kW charging, the power demand stays within the acceptable limits, but the coincidence factor increases from 0.2 to 0.63-0.93.
Consensus-Based Distributed Control of a Multilevel Battery Energy Storage System

Battery Energy Storage Systems (BESS) based on modular multilevel cascaded topologies allow splitting the battery array into the converter sub-modules, improving efficiency and reliability of the solution. Modular converters can perform active balance of the battery packs and regulation of the output power through the use of multi-objective controllers commanding the power of each sub-module accordingly. However, despite the hardware modularity of multilevel BESS, the controller is usually based on centralized designs, which present issues in terms of computational load and scalability. This paper proposes a two-layer distributed control scheme, based on a high-level consensus algorithm to perform the State of Charge (SoC) balance and low-level individual multi-variable controllers to regulate the operation of the converter sub-modules. The controller was implemented in a cascaded topology, where each sub-module determines and regulates its output power considering the information of its neighbours. The proposed solution allows to fully exploit the modularity of the converter, distributing the control units in the power modules to improve the overall flexibility and scalability of the system. Simulations results validate the operation of the proposed system, showing how the converter sub-modules distribute its power outputs to equalize the SoC levels. Furthermore, the distribution of the multi-variable controllers reduces the computational burden, as each unit just regulates the operation of the respective sub-module.
Distributed Optimal Formation Control with Hard Constraints on Energy and Time

This paper studies distributed optimal formation control with hard constraints on energy levels and termination time, in which the formation error is to be minimized jointly with the energy cost. The main contributions include a globally optimal distributed formation control law and a thorough analysis of the roles of energy, time and control in limiting formation control feasibility. These results characterize and quantify explicitly how energy levels, task termination time, steady-state error tolerance, as well as the network topology may impose inherent limitations on the feasibility of the formation control mission. Most notably, the lower bounds on the termination time and the minimum energy levels are derived, which are given in terms of the initial formation error, the parameter of steady-state error tolerance, and the largest eigenvalue of the Laplacian matrix.
Relative Humidity Dependence of Pt Utilization in Polymer Electrolyte Fuel Cell Electrodes: Effects of Electrode Thickness, Ionomer-to-Carbon Ratio, Ionomer Equivalent Weight, and Carbon Support

Pt utilization, i.e., the ratio of the electrochemically active Pt surface area of an MEA to the total Pt surface area of the catalyst, was analyzed at 20-100% RH, and 40-80°C by a newly developed technique using CO as a probe. The Pt utilization significantly decreased with the decreasing relative humidity (RH) for a Pt/C catalyst in which the Pt particles are supported not only on the outer surface, but also within the pores of the carbon particles, while it remained high even at a low RH for the samples with Pt particles supported only on the outer surface of the carbon particles. We presume that this is because the Pt particles within the pores are not in contact with the ionomer therefore, the protons cannot access them at a low RH.
Analysis of AC Loss Contributions From Different Layers of HTS Tapes Using the A−V Formulation Model

This article presents a numerical model based on the definition of the scalar and vector potentials to calculate the ac losses generated in a high-temperature superconductor coated conductor tape by an ac transport current and by an ac external magnetic field applied with a generic orientation. The material characteristics of the different layers of a reference tape are included in the model equations, which allow analyzing the differences between a model including only the superconducting layer or all layers of the coated conductor. The model is applied to study the dependence of ac losses on the amplitude of the transport current and magnetic field (applied individually or combined) and their frequency. The numerical results are compared with analytical formulations, with a second numerical model based on the H-formulation, and with experimental results. The conditions at which the contribution of the nonsuperconducting layers to the total ac losses of the tape become relevant are analyzed. In particular, the dependence of losses on the frequency of the ac external magnetic field applied at different orientations, is investigated. The current density distribution computed on the conductor cross-section is used to explain the trend of the calculated losses.