Miracle-dz/UniFAR-FTUs huggingface dataset:
Coordination of multi‐agent systems with arbitrary convergence time

This paper proposes ﬁxed-time solutions to several problems in multi-agent systems. First, a ﬁxed-time consensus protocol is investigated and under the assumption that the lower bound of smallest positive eigenvalue of the Laplacian matrix is known in advance, the consensus time of the protocol can be arbitrarily selected. Second, the consensus law to attenuate bounded disturbances acting on the system is modiﬁed. Third, the ﬁxed-time design method is applied to the orientation alignment and network localization problems. Finally, several simulations are provided to support the mathematical analysis.
A Reference Model Assisted Adaptive Control Structure for Maglev Transportation System

Maglev transportation system is become a hot topic for researchers because of the distinctive advantages, such as frictionless motion, low power consumption, less noise, and being environmentally friendly. The maglev transportation system’s performance gets sufficiently influenced by the control method and the magnetic levitation system’s dynamic performance, which is a critical component of the maglev transportation system. The Magnetic Levitation System (MLS) is a group of unstable, nonlinear, uncertain, and electromagnetically coupled practical application. Control objective of this study is to design a position stabilizing control strategy for Magnetic Levitation system under extreme uncertain parametric conditions using a reference model governed by a reference stabilizer and nonlinear adaptive control structure. After successful tuning the reference stabilizer with and without time-varying payload disturbance, the tracking-error dynamics are obtained in the presence of both matched and mismatched types of parametric uncertainties. Next, the close-loop stability theorem is formulated for Lyapunov stability analysis to get the design constraints, parameter update laws, and adaptive control law. Numerical simulations performed for a high range of parametric violations check the control design’s efficacy. The performance robustness gets confirmed by comparing the results with the nonlinear control approach. The MLS gets performance recovery and settles within safe limits in few seconds using the proposed methodology. However, the nonlinear controller faces permanent failure in stabilizing the MLS.
Influence of Photo-Deposited Pt and Pd onto Chromium Doped TiO2 Nanotubes in Photo-Electrochemical Water Splitting for Hydrogen Generation

Hydrogen (H2) is considered as an ideal fuel for the future. The photo-electrochemical (PEC) water splitting employing semiconducting materials and induced irradiation, preferably of solar spectrum, presents a viable route for H2 production. In this work, self-ordered chromium-doped TiO2 nanotube (CT) was fabricated using in-situ electro-anodization. CT surface modification was then performed by photo-deposition of Pt and Pd particles, producing Pt-CT and Pd-CT catalysts, respectively. Their morphological features, crystallinity, surface composition, and optical absorption have been inspected by scanning electron microscopy (SEM), X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDX), Raman, and UV–vis absorption spectroscopy. Linear sweep voltammetry, chronoamperometry, and open circuit potential methods have been applied to study PEC activities of Pt-CT and Pd-CT catalysts in a form of electrodes. It was found that Pt-CT and Pd-CT electrodes possess excellent photo-generated electron/hole (e−/h+) separation and transport properties. The enhanced photocurrent responses of 4 and 3 times more than that of CT are revealed for Pt-CT and Pd-CT, respectively. The activity of as-prepared Pt-CT and Pd-CT catalysts was then tested for H2 generation. The maximum amount of the evolved H2 followed decreasing order: 1.08 > 0.65 > 0.26 mL cm−2 h−1 for Pt-CT, Pd-CT, and CT electrodes, respectively, clearly showing the positive contribution of photo-deposited (nano)particles onto CT surface.
Balancing Excitation and Inhibition of Spike Neuron Using Deep Q Network (DQN)

Deep reinforcement learning which involved reinforcement learning with artificial neural networks allows an agent to take the best possible actions in a virtual environment to achieve goals. Spike neuron has a non-differentiable spike generation function that caused SNN training faced difficulty. In order to overcome the difficulty, Deep Q Network (DQN) is proposed to act as an agent to interact with a custom environment. A spike neuron is modelled by using NEST simulator. Rewards are given to the agent for every action taken. The model is trained and tested to validate the performance of the trained model in order to attain balance the firing rate of excitatory and inhibitory population of spike neuron. Training result showed the agent able to handle the environment. The trained model capable to balance the excitation and inhibition of the spike neuron as the actual output neuron rate is close to or same with the target neuron firing rate. The average percentage error of rate of difference between output and target neuron rate for 5 episodes achieved 0.80%.
A data‐independent 9T SRAM cell with enhanced ION/IOFF ratio and RBL voltage swing in near threshold and sub‐threshold region

The conventional 8T SRAM cell with isolated read port is suggested as an alternative to overcome the read‐write conflicts associated with 6T SRAM cell. However, in near threshold and sub‐threshold regions, 8T cell performance is limited by reduced ION/IOFF ratio, deteriorated RBL voltage swing, data dependency, and higher read failures, although the existing SRAM cells address some of these issues but still suffer from degraded performance due to the trade‐off between leakage and read currents. In this paper, a 9T SRAM cell with novel read port is proposed that aims for low and data‐independent leakages, high ION/IOFF ratio, and large RBL voltage swing in near threshold and sub‐threshold regions. The performance of the proposed cell is compared with 7T, 8T, 9T, and 10T cells at 32 nm technology node by simulating a column of 128 cells to demonstrate its versatility over others. The proposed cell shows enhanced ION/IOFF ratio (71.2X), large RBL voltage swing and data‐independent leakages at VDD = 0.3 V in comparison to the conventional 8T SRAM cell. The results at different PVT corners are also captured to validate the impeccable performance of the proposed cell irrespective of operating conditions.
Theoretical and Practical Considerations Regarding the Use of Conditional Probability Assessment Algorithm in Early Warning Tasks Aimed to Prevent Acts of Terrorism and Violent Extremism

Abstract Over the last decade, the diversified spectrum of hybrid threats forced security agencies around the world to refine the tools used by intelligence analysts in early warning tasks. Thus, in order to anticipate and prevent violent acts, law enforcement analysts from the departments responsible for prevention and countering terrorism, violent extremism, and radicalization that lead to terrorism are constantly striving to identify tools and methods that are capable to measure the psychosocial–behavioral characteristics of individuals in terms of mathematical probabilities. Therefore, risk assessment tools targeting terrorists and violent extremists are a major topic of interest for international security experts and law enforcement professionals. In such context, the present study proposes a tool to assess conditional probabilities, built on the Bayes’ rule, which is based on a subjective way of defining probability. Bayes’ rule allows analysts to correctly quantify their estimates, which are often qualitative, subjective, and based on a limited number of indices and observations. The risk assessment tools developed for common violent crimes and for members of the organized crime groups should not be used for assessing terrorists, violent extremists, or individuals suspected of engaging in ideologically motivated violent actions, nor vice versa. Specific risk assessments with relevant indicators should be used separately for regular violent offenders and for terrorists and extremists.
Understanding food experience in sharing-economy platforms: insights from Eatwith and Withlocals

ABSTRACT This study aims to identify and determine the dimensions of food experience using user-generated content on two sharing-economy websites as well as to offer insights into factors affecting food tourists’ evaluation of travel experiences at a culinary destination, Istanbul. For this purpose, conventional content analysis is conducted on 459 food-experience reviews on sharing-economy websites, namely, EatWith and WithLocals. Results reveal 26 items and four themes, that is, knowledge, authenticity, local hospitality, and social interaction. Theoretical and managerial implications as well as limitations are discussed, and suggestions for further research are provided.
Comparative phytochemical profile of Indoneesiella echioides (L.) Nees leaves using GC-MS

Indoneesiella echioides (or) Andrographis echioides (L) Nees is an important herb widely distributed in south India. This is commonly known as False water willow. Indoneesiella echioides (L) Nees is used as in traditional Indian medicine. The leaf juice of this plant is used to cure fever. Different pharmacological properties of Indoneesiella echioides have already been reported. Thus, the present study was performed to investigate the preliminary phytochemical screening, separation, identification of compounds and compare the phytochemical composition of various fraction of Indoneesiella echioides using gas chromatography-mass spectrometry. The plant was extracted for various solvents in increasing order of polarity from using n-hexane, chloroform, ethyl acetate, acetone, ethanol, butanol and methanol. The result obtained after GC-MS studies were confirmed by spectral analysis.
A Green Data Management Layer in Industrial IoT

All Nowadays, industry 4.0, which aims at transforming industrial manufacturing systems to the smart factories, has gained considerable attention from both industry and academia. The Industrial Internet of Things (IIoT) plays a primordial role to achieve the objectives of Industry 4.0, by enabling the Cyber-Physical Production Systems (CPPS) to communicate and cooperate with each other and with humans both internally and across the participants of the supply chain. A communication model used commonly in IIoT networks consists of the publish-subscribe model, in which the data generated by sensor nodes are cached in a central controller to be subsequently consumed by actuator nodes. However, the traditional centralized data management is inappropriate for real application because of its communication overhead and inability to cope with strict delay requirements of IIoT applications and energy constraints of IIoT networks. To address these issues, we propose a Green Data Management Layer (GDML), which caches data distributedly in some IoT nodes, called proxy nodes, so that the network energy consumption is optimized while the constraints on data access latency and IoT nodes’ cache capacity are met. In this measure, the extra IoT nodes not involved in data transmission nor in data caching are switched off to prolong the battery lifetime of constrained IoT nodes. We modeled the aforementioned optimization problem as an Integer Linear Programming (ILP) and solved it using the CPLEX tool. The obtained results show that the proposed GDML outperforms significantly alternative solutions in terms of overall energy consumption, energy consumption devoted to data transmission, energy consumption devoted to keeping active the involved nodes, while the constraints on data applications latency and the cache size of IoT nodes are satisfied.
Indicators of Computer-Mediated Communication Affecting Remote Employee Engagement

In this study, the indicators of computer-mediated communication that affect engagement among remote knowledge workers were analyzed. After conducting an online survey of 133 remote knowledge workers in the U.S., a multiple regression statistical model was used to answer the research question seeking the extent to which empathy, expressiveness, and motivation contributed to remote employee engagement. Our findings suggested the existence of a statistically significant relationship between the independent variables Empathy, Expressiveness, and Motivation, and the dependent variable Engagement. This study contributed to knowledge by filling the gap in current literature as no previous attempt had been made on analyzing the relationship between engagement and the items identified here. Our findings have implications for researchers and practitioners in that they may assist in guiding future research and work design for remote knowledge workers in the digital and pandemic era. This study can help managers identify remote workers who need the most help. This comes from the observation that remote employees work away from their managers and colleagues and the absence of casual conversations that tend to provide directions or remind them of things to do could hinder their performance in the absence of self-determination.
Investigation of the CO 2 Sequestration by Indirect Aqueous Carbonation of Waste Cement

Mineral carbonation of CO2 with fine-grained waste cement was investigated using NH4Cl as a recyclable extracting agent. The amount of calcium extracted with NH4Cl was not as high as with more commonly used extraction agents such as HCl and CH3COOH. NH4Cl also exhibited high selectivity in the calcium extraction process, such that calcium ions comprised over 99% of the leaching solution. Another positive benefit of using NH4Cl was that precipitation of calcium carbonate by CO2 injection was possible without the addition of basic reagents. Moreover, the NH4Cl regenerated during carbonation can be reused for calcium extraction. However, test results using regenerated NH4Cl solution in a cyclic fashion revealed that the process was not perfectly cyclic, but rather the calcium amount after precipitation increased as the cycle proceeded. The geochemical computer simulation PHREEQC was utilized to gain better insight into the cyclic mineral carbonation processes using NH4Cl solution. The simulation was based on thermodynamic equilibrium so that the amount of Ca in the solution fluctuated between specific values in a periodic fashion, unlike the experimental results of calcium accumulation in the extraction solution. One reason for this phenomenon was the kinetic/thermodynamic balance controlled by the amount of Ca2+ and CO2 present in the solution. However, it was feasible to use a geochemical model to evaluate the mineral carbonation process with the correction factors since the deviation between the experimental and the simulation results remained fairly constant throughout the cycle.
Permian lamprophyres from the Western Carpathians: a review

Abstract Calc-alkaline lamprophyres from the Western Carpathians occur as dykes and sills in the crystalline complexes (predominantly granites and gneisses) of the Tatric Unit. Some of the lamprophyre dykes have been strongly overprinted by tectonism and hydrothermal fluid ingress. They have a similar mineralogical composition, and they are comprised of clinopyroxene, amphibole, biotite and plagioclase. Based on their modal composition, they can be classified as spessartites and kersantites, and based on their geochemical composition, most of them are of calc-alkali type. Lamprophyres from individual core complexes (e.g. the Malá Fatra Mountains and Nízke Tatry Mountains) exhibit variable Nb, Ta and Sr–Nd isotope signatures. These differences are probably due to compositional variations in the mantle source and/or the lower crust at the site of lamprophyre melt generation, or variable incorporation of crustal material. The age of the lamprophyres is Permian (c. 265 Ma) based on U–Pb LA-ICP-MS dating of apatite micro-phenocrysts.
A novel motor imagery EEG decoding method based on feature separation

Objective. Motor imagery electroencephalography (EEG) decoding is a vital technology for the brain–computer interface (BCI) systems and has been widely studied in recent years. However, the original EEG signals usually contain a lot of class-independent information, and the existing motor imagery EEG decoding methods are easily interfered by this irrelevant information, which greatly limits the decoding accuracy of these methods. Approach. To overcome the interference of the class-independent information, a motor imagery EEG decoding method based on feature separation is proposed in this paper. Furthermore, a feature separation network based on adversarial learning (FSNAL) is designed for the feature separation of the original EEG samples. First, the class-related features and class-independent features are separated by the proposed FSNAL framework, and then motor imagery EEG decoding is performed only according to the class-related features to avoid the adverse effects of class-independent features. Main results. To validate the effectiveness of the proposed motor imagery EEG decoding method, we conduct some experiments on two public EEG datasets (the BCI competition IV 2a and 2b datasets). The experimental results comparison between our method and some state-of-the-art methods demonstrates that our motor imagery EEG decoding method outperforms all the compared methods on the two experimental datasets. Significance. Our motor imagery EEG decoding method can alleviate the interference of class-independent features, and it has great application potential for improving the performance of motor imagery BCI systems in the near future.
Comparative Evaluation for an Improved Direct Instantaneous Torque Control Strategy of Switched Reluctance Motor Drives for Electric Vehicles

Due to the expected increase in the electric vehicles (EVs) sales and hence the increase of the price of rare-earth permanent magnets, the switched reluctance motors (SRMs) are gaining increasing research interest currently and in the future. The SRMs offer numerous advantages regarding their structure and converter topologies. However, they suffer from the high torque ripple and complex control algorithms. This paper presents an improved direct instantaneous torque control (DITC) strategy of SRMs for EVs. The improved DITC can fulfill the vehicle requirements. It involves a simple online torque estimator and a torque error compensator. The turn-on angle is defined analytically to achieve wide speed operation and maximum torque per ampere (MTPA) production. Moreover, the turn-off angles are optimized for minimum torque ripples and the highest efficiency. In addition, this paper provides a detailed comparison between the proposed DITC and the most applicable torque control techniques of SRMs for EVs, including indirect instantaneous torque control (IITC), using torque sharing function (TSF) strategy and average torque control (ATC). The results show the superior performance of the proposed DITC because it has the lowest torque ripples, the highest torque tor current ratio, and the best efficiency over the low and medium speed ranges. Moreover, the comparison shows the advantages of each control technique over the range of speed control. It provides a very clear overview to develop a universal control technique of SRM for EVs by merging two or more control techniques.
Lived Experience of Online Teaching During the COVID-19 Pandemic: Implications for Curriculum and Teaching

As the COVID-19 pandemic has affected all sphere of human life and the schools across the country are closed due to the risk of spreading the virus, online teaching has become a major alternative pedagogical strategy among the private schools in particular. This article reports a study that explores how private school teachers perceive and adopt technological learning, how they transfer their technological knowledge and skills into the online classroom and how they self-assess their practices. This is done through a phenomenological study focusing on the meaning the participants make from their lived experience on ‘technological learning and application’ in the face of the pandemic. The study found that dealing with uncertainty and fear of the COVID-19 and the additional pressure for doing online teaching amidst the crisis evoked frustration and anguish among the teachers. Despite a number of challenges and crisis, teachers, however, learn to deal with the technological challenges and manage to run the class through virtual mode. Nevertheless, they assess the online delivery is not effective due to the various constraints. The study suggests for ensuring the access of ICT resources and facilities to both teachers and students, sufficient training to the both groups and digitalize curricular materials for the effective implementation of virtual learning.
Identificazione ed origine di patine ad ossalato sui monumenti bronzei: il caso del portale centrale del duomo di Loreto (AN)

Analyses of corrosion products of bronze sculptures and statues involved mineralogy, using x-ray diffraction (XRD), and chemistry, using PY-GC and gas chromatography-mass spectrometry (GC-MS). Gypsum, copper, and calcium oxalates are the main components, together with organic materials. The origin of copper oxalate is related to a cleaning operation with oxalic acid.
Spray-formed polyurea composites filled with basalt powder as inorganic waste filler

The effect of basalt powder (BP) as a waste filler on the properties of polyurea (PUA) has been investigated. The BP was added into a two-component quick-setting polymeric matrix and formed by spray forming. The characterisation of the composites manufactured using waste inorganic particulate filler was performed using mechanical testing (static tensile test and hardness measurement), thermomechanical analysis (Vicat softening temperature – VST and dynamic thermomechanical analysis – DMA) and thermogravimetric analysis (TGA). Properties of the pure PUA and PUA-composites were discussed in reference to structural analysis proceeded by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). The addition of BP into PUA allows improving the overall performance of spray-formed material properties. Composite filled with 20 wt-% of the BP showed the most beneficial set of mechanical properties with simultaneously improved thermomechanical and thermal properties.