Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal robust adaptive fuzzy backstepping control of electro-hydraulic servo position system

In this paper, an optimal robust adaptive fuzzy backstepping control is presented to the position control of the electro-hydraulic servo (EHS) system in the presence of structured and unstructured uncertainties. Initially, the robust control using the backstepping technique is presented to overcome the existing uncertainties in the dynamic equations. Mathematical proof demonstrates that the closed-loop system in the presence of uncertainties has a global asymptotic stability. Then, to overcome the chattering problem, a very simple fuzzy approximator is presented where it approximates the bounds of the uncertainties. Although the proposed robust fuzzy backstepping control has a desirable performance, it has no mathematical analysis to prove the stability of the closed-loop system. Therefore, to solve this problem, the proposed fuzzy approximator has been transformed into a one-law adaptive fuzzy approximator with a single-input single-output fuzzy rule. Mathematical analysis illustrates that the closed-loop system in the presence of uncertainties has a global asymptotic stability under the proposed robust adaptive fuzzy backstepping control. Furthermore, a novel modified harmony search algorithm (MHSA) has been developed, by using the original harmony search algorithm (OHSA) as an optimization technique, to achieve the optimal values of the membership functions and the control coefficients. Finally, a comparative study has been conducted between the proposed control scheme under the MHSA and the OHSA, and other existing advanced control approaches to verify the effectiveness of the proposed control. Results show that the proposed control scheme under the MHSA can suppress the chattering problem and reduce the disturbances effectively while ensuring that the performance is tracked.
Immersive Virtual Reality Exergame Promotes the Practice of Physical Activity in Older People: An Opportunity during COVID-19

Life expectancy has increased in recent years. Physical activity has been postulated as a key element in active aging strategies. However, adherence to physical exercise programs has traditionally been low among the elderly, and the current situation of the COVID-19 pandemic has added extra impediments. Immersive virtual reality (IVR) devices could motivate this population to practice exercise. This study aimed to analyse the use of IVR exergames as a tool to facilitate physical exercise in older people. Four healthy older adults (males, 65–77 years) participated in the study. They carried out two exergaming sessions with HTC Vive ProTM. Outcomes were evaluated using the Simulator Sickness Questionnaire (SSQ), System Usability Scale (SUS), Game Experience Questionnaire (GEQ post-game module), an ad hoc satisfaction questionnaire, and perceived effort. All participants completed the sessions without adverse effects, with no SSQ symptoms reported. SUS scores were high in both sessions (SUS > 85/100). Post-game GEQ scores were 3.08–3.41/4 (positive experiences) and 0.08–0.16/4 (negative experiences). Opinions showed high levels of satisfaction with the experience. Exergaming programs, based on commercial head-mounted displays, are a feasible alternative to traditional senior exercise, and could be a solution to the current situation that has arisen from the impact of the COVID-19 pandemic.
Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews

The life cycle of wind turbines depends on the operation and maintenance policies adopted. With the critical components of wind turbines being equipped with condition monitoring and Prognostics and Health Management (PHM) capabilities, it is feasible to significantly optimize operation and maintenance (O&M) by combining the (uncertain) information provided by PHM with the other factors influencing O&M activities, including the limited availability of maintenance crews, the variability of energy demand and corresponding production requests, and the long-time horizons of energy systems operation. In this work, we consider the operation and maintenance optimization of wind turbines in wind farms woth multiple crews. A new formulation of the problem as a sequential decision problem over a long-time horizon is proposed and solved by deep reinforcement learning based on proximal policy optimization. The proposed method is applied to a wind farm of 50 turbines, considering the availability of multiple maintenance crews. The optimal O&M policy found outperforms other state-of-the-art strategies, regardless of the number of available maintenance crews.
Design of a Fuzzy Logic Controller for Short-Term Load Forecasting With Randomly Varying Load

Short-term load forecasting (STLF) is an integral component of energy management systems. In this paper, fuzzy logic-based algorithm is used for short-term load forecasting. The load changes over time and the goal is to satisfy the shift in demand and to maintain a fault as low as possible between the reference and real powers. The error in the load demand in mega-watt (MW) is compared with proposed technique as well as conventional methods. Three cases were investigated in which the load changes were 1) more random in nature, but the variance to the reference was more; 2) the random load changes were simpler, but a little different from the reference; and lastly, 3) the load changing was random, and the reference deviation was maximum. The results are analyzed for different load changes, and the corresponding results are verified using MATLAB. The deviation of the error value in load response is less experienced with a fuzzy logic controller than with a traditional system, and in fewer iterations, the objective function is also achieved.
Effect of Injectable-Platelet Rich Fibrin on marginal adaptation of Bioactive Materials Used as Direct Pulp Capping; An Experimental Animal Study

Introduction: Vital pulp therapy has been known as one of the treatment options to preserve pulp vitality after being exposed by trauma or caries. Aim: This experiment explored the effect of injectable-Platelet Rich Fibrin on marginal adaptation of two pulp capping agents (Mineral Trioxide Aggregate and Bioactive Bone Graft). Materials and methods: A total of 64 teeth were used out of 8 healthy male beagle dogs. The teeth were randomly assigned into four groups, they were exposed and capped with different capping agents. Group A; capped with Mineral Trioxide Aggregate (MTA), Group B; capped with MTA+ i-PRF, Group C; capped with Bioactive Bone Graft (BBG), Group D; capped with BBG+i-PRF. Finally the access cavity was restored with Intermediate Restorative Material (IRM). At each predetermined interval, the dogs were sacrificed (1 month, and 3 months). The samples were then prepared for electron microscopic scanning evaluation. To compare between the gap percentage of four groups at each interval, Kruskal-wallis test; was used. Mann-Whitney U test; was used to pair-wise comparison when Kruskal-wallis test is significant. Bonferroni’s correction was utilized for the pair-wise comparisons. Statistical significance was considered at P < . 05. Results: The data revealed that after one and three months the best values were recorded in groups B (MTA+ i-PRF) and D (BBG+ i-PRF), in relation to the lowest gap area between the capping materials and dentin, followed by group C (BBG), with the least value recorded in group A (MTA). Conclusion: the findings from the current study suggested that i-PRF provided a better marginal adaptation of either MTA or BBG to the pulp and dentin, which improved with time from one month to three months.
A self-healing fluorine-free superhydrophobic cotton fabric under heat stimulation

Superhydrophobic cotton fabrics have been widely explored for their excellent properties, although there are still a few limitations in the practical applications for the reasons of poor environmental performance and short service life. In this study, the hexadecyltrimethoxysilane emulsion was prepared using O2-plasma and ultrasound for the superhydrophobic functional finishing of raw cotton fabric by immersion method. The prepared cotton fabrics showed a superior hydrophobicity with water contact angles larger than 156.0°, as well as an excellent self-healing ability. This self-healing ability was gained by simple heating treatment, which could restore the superhydrophobicity of the hydrophilic cotton fabric damaged by abrasion or O2-plasma treatment. The superhydrophobic cotton fabrics demonstrated excellent washing durability, which can withstand 50 times accelerated laundering cycles. Besides this, the superhydrophobic coatings also displayed fair chemical stability in strong acidic and alkaline solutions. The prepared nonfluorinated superhydrophobic cotton fabric with self-healing ability shows a potential for practical functional textile application.
Simulation Assisted Current Density Monitoring for Lithium-ion Batteries in Electric Vehicles

During the transformation of electrical energy in a grid-scale network, using an optimal energy storage system for balancing the power utilization and generation is an important challenge. Using batteries for this purpose in grid-level systems are highly recommended due to the flexibility in installation, modularization and fast response. Comparing to the other types, Lithium-ion batteries (LIBs) are a more common choice in industry because of their higher energy efficiency and density, inexpensive manufacturing cost and long life cycle. To use the batteries optimally, reliable state of health (SoH) monitoring solutions are required to be included in the battery management system (BMS). This paper proposes a simulation assisted electrode and electrolyte current density monitoring for lithium-ion batteries that can considerably increase the SoH estimation accuracy. The proposed method is realized through the fusion of the information from the magnetic field sensors together with the online simulation of the battery dynamic model, in real-time. The battery model of an electric vehicle is developed in COMSOL modelling software and the data fusion is implemented on dSPACE Microlabbox real-time simulator. The results confirm that the proposed monitoring solution can be potentially used to provide a highly accurate estimation system for a Lithium-ion cell.
Insilico Molecular Docking Studies of Volatile Compounds Identified by GC-MS from Tagetes Species Against Mamestra brassicae (Linnaeus, 1758)

Plants evolved to be a potential source of pharmacologically active compounds that are being widely accepted as insect repellent compounds for generations. Products of natural origin are mostly preferred over synthetic compounds because of fewer side effects on human health and the environment, have the potential to be produced locally, cost-effective, and are proved to be more efficient. They are best suited in organic food production and can play a much greater role in developing countries as a new class of eco-friendly products for controlling pests. In turn, the development of repellents is desirable alternatives to synthetic chemical insecticides for controlling pests. In the process of continual search for insect-based repellents of natural origin, a wide number of Tagetes species have been archived and all parts of this plant from root to seed possess a range of phytochemicals that are responsible for the repellent activity. The present study concentrates on the identification of active volatile compounds from Tageteserecta leaves by Gas chromatography-mass spectrometry (GC-MS) analysis and further evaluation through molecular docking studies of identified compounds against Mamestra brassicae.
Determining the Influencing Factors of COVID 19 on Mental Health Using Neural Network

In 2020 there is outbreak of the pandemic COVID 19. Due to the pandemic most of the countries enforced full lockdown. During lockdown the mental health of the common people of all ages are affected. The study aims to investigate the factors which influence the mental health. Mental health is influenced by the various factors. In this research the artificial neural network is used to predict the factors influencing the mental health due to COVID 19 pandemic. Experimental result showed that artificial neural network not only has better accuracy up to 81.5% but also faster training process. Based on these results, the determinant factors of mental health among people are: frustration, fear, sudden change in daily life style, anxiety to perceive changes as a challenge.
Spatio-Temporal Correlation-Based False Data Injection Attack Detection Using Deep Convolutional Neural Network

There are lots of cyber-attack, especially false data injection attacks, in modern power systems. This attack can circumvent traditional residual-based detection methods, and destroy the integrity of control information, thus hindering the stability of the power system. In this paper, a novel Spatio-temporal detection mechanism is proposed to evaluate and locate false data injection attacks. In the proposed method, temporal correlation and spatial correlation are analyzed by cubature Kalman filter and Gaussian process regression, respectively, to capture the dynamic features of state vectors. Then, a deep convolutional neural network is trained to depict the functional relationship between Spatio-temporal correlation functions and the output, which is set as the detection indicator to access whether the power system under attack or not. Furthermore, the performance of the proposed mechanism is evaluated with comprehensive numerical simulation on IEEE 39-bus test system. The results of the case studies showed that the proposed method can achieve 99.84%-100% accuracy.
THE TRACKING OF MODAL PARAMETERS FOR A REINFORCED CONCRETE BUILDING DURING LOW-MEDIUM INTENSITY EARTHQUAKES

This paper presents the results of the dynamic monitoring carried out on a school building in central Italy during the seismic sequence following the first main shock of the 2016 Central Italy earthquake. The building is located in the historical centre of Camerino and consists of a reinforced concrete frame structure with masonry infill walls. The school, dating back to the 60s, underwent seismic retrofit in 2013 through the construction of 2 dissipative towers, a recent patented system for seismic protection of buildings. In August 2016 a dynamic monitoring system was installed on the building, positioning an array of accelerometers both on the top two floors of the structure and on the foundation level; this made it possible to record the building response to the aftershocks that occurred during the monitoring period, and the corresponding seismic input. The dynamic characteristics of the structure during the monitoring period are identified starting from the response of the structure subjected to the seismic swarm following the main event, and to the environmental vibrations between two subsequent events. Although during the monitoring days the building did not suffer any damage, the response of the structure proved to be nonlinear and strongly dependent on the amplitude of the accelerations to which it was subjected; in this work, a procedure to linearize the structural response and carry out the dynamic identification in terms of modal parameters starting from the non-stationary response of the structure is proposed.
Impairment of Assets and Market Reaction during COVID-19 Pandemic on the Example of WSE

The main task of the article is to examine the impact of the reported impairment of assets (IoA) on the market reaction of investors on the Warsaw Stock Exchange [WSE] in the crisis condition caused by the COVID-19 pandemic. There is a need to verify whether the disclosure of this information in the period of economic downturn will cause a similar negative reaction as in previous topics in this area. Research undertaken in this article helps identify the rules of behaviour (in the short term) whether the reaction of investors on updating the company’s assets in crisis conditions is different than in times of prosperity. The main hypothesis will be verified using the event study methodology. It allows to verify whether the upcoming information about IoA during the COVID-19 pandemic confirms an existence of statistically significant negative abnormal returns. Based on the 55 cases of current reports informing about IoA, which were submitted to the investors in the year 2020 and finally qualified for the research sample, I have not observed statistically significant negative abnormal returns on the adjacent days. The results are different from those obtained by researchers who study the market reaction to the IoA under non-crisis conditions of the economy.
A 2.1 mW 2 MHz-BW 73.8 dB-SNDR Buffer-Embedded Noise-Shaping SAR ADC

This paper presents a buffer-embedded noise-shaping SAR ADC, whose input buffer separates the capacitive DAC (CDAC) and the sampling capacitor (CS) at the input and output of the input buffer. This compensates for the non-linearity of the input buffer and reduces the CS value, resulting in a significant power saving in the input buffer. This buffer-embedded architecture enables the effective implementation of the following passive loop filter and enhances energy efficiency. A bootstrapping switch in the feedback CDAC is coupled to the output of the buffer, thereby avoiding a signal dependency due to the parasitic capacitance of the switch. The buffer-embedded noise-shaping SAR ADC occupies 0.08mm2 in a 65 nm CMOS process and features a parasitic input capacitor of 0.2 pF. It achieves 73.8 dB SNDR, 77 dB DR and 87.3 dB SFDR in a 2 MHz bandwidth without any calibration. Including the power consumption of the input buffer, the ADC consumes only 2.1 mW.
Pengaruh Inokulasi Glomus mosseae terhadap Pertumbuhan dan Aktivitas Antioksidan pada Artemisia cina Berg ex Poljakov

Artemisia cina is a wild herbaceous plant that has secondary metabolites as a source of antioxidants. The addition of mycorrhizal species Glomus mosseae can increase the antioxidant activity of plants Artemisia cina. This study aims to determine the effect of mycorrhizal Glomus mosseae on the growth and content of antioxidant compounds in Artemisia cina. The study used a completely randomized design (CRD), with four levels of treatment, namely: media without G. mosseae (control), media with G. mosseae 5,10 and 15 g with 5 replications for each treatment. Parameters observed were plant height, number of leaves, 5th leaf length and width, wet and dry weight, root length, percentage of mycorrhizal colonization, chlorophyll content, carotenoids, flavonoids, phenols were also observed. Antioxidant activity was determined by the DPPH method. The results showed that the treatment Glomus mosseae with a dose of 15 g was significantly different from the control on plant growth parameters, number of leaves, root length, wet weight and dry weight of plants, and percentage of mycorrhizal colonization in plants. Treatment of Glomus mosseae 15 g on the test results of antioxidant compounds also showed the highest content of chlorophyll, carotenoids, phenols and flavonoids from the statistical test results found in the 15 g treatment, respectively, namely 6,405 g/ml; 0.766 g/ml; 70.9 g/ml; and 335 g/ml had a significant difference to the control treatment. While the results of the antioxidant activity test using the DPPH method showed that the control treatment had the highest antioxidant activity with an ICvalue50 of 9,494 g/ml. The results showed that the mycorrhizal species of G. mosseae increased the growth and content of antioxidant compounds inplants A. cina.
Automatic Estimation of Food Intake Amount Using Visual and Ultrasonic Signals

The continuous monitoring and recording of food intake amount without user intervention is very useful in the prevention of obesity and metabolic diseases. I adopted a technique that automatically recognizes food intake amount by combining the identification of food types through image recognition and a technique that uses acoustic modality to recognize chewing events. The accuracy of using audio signal to detect eating activity is seriously degraded in a noisy environment. To alleviate this problem, contact sensing methods have conventionally been adopted, wherein sensors are attached to the face or neck region to reduce external noise. Such sensing methods, however, cause dermatological discomfort and a feeling of cosmetic unnaturalness for most users. In this study, a noise-robust and non-contact sensing method was employed, wherein ultrasonic Doppler shifts were used to detect chewing events. The experimental results showed that the mean absolute percentage errors (MAPEs) of an ultrasonic-based method were comparable with those of the audio-based method (15.3 vs. 14.6) when 30 food items were used for experiments. The food intake amounts were estimated for eight subjects in several noisy environments (cafeterias, restaurants, and home dining rooms). For all subjects, the estimation accuracy of the ultrasonic method was not degraded (the average MAPE was 15.02) even under noisy conditions. These results show that the proposed method has the potential to replace the manual logging method.
From Simulated to Visual Data: A Robust Low-Rank Tensor Completion Approach Using ℓp-Regression for Outlier Resistance

Low-rank tensor completion (LRTC) that aims to restore the latent clean data from an incomplete and/or degraded observation, shows promising results in ubiquitous tensorial data completion applications. Most tensor completion approaches are vulnerable to outliers since their derivations are based on <inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula>-space to be robust against Gaussian noise. In this work, to tackle this issue, <inline-formula> <tex-math notation="LaTeX">$\ell _{p}$ </tex-math></inline-formula>-regression <inline-formula> <tex-math notation="LaTeX">$(0 < p < 2)$ </tex-math></inline-formula> is employed to achieve outlier resistance, where a factored form of tensor train (TT)-format representation is regularized by the low-TT-rank prior to exploit the inter-fibers correlation. On the basis of that, an effective iterative <inline-formula> <tex-math notation="LaTeX">$\ell _{p}$ </tex-math></inline-formula>-regression TT completion method (referred to <inline-formula> <tex-math notation="LaTeX">$\ell _{p}$ </tex-math></inline-formula>-TTC) is proposed, with the advantage of not requiring the hard-to-determine user-defined weights in TT rank model. Extensive experiment results are presented to demonstrate the outlier resistance of the proposed <inline-formula> <tex-math notation="LaTeX">$\ell _{p}$ </tex-math></inline-formula>-TTC, and showing the effective and superior performance in both bistatic MIMO radar localization and color image inpainting and denoising, compared with state-of-the-art tensor completion approaches.
A Data-Driven Digital Application to Enhance the Capacity Planning of the COVID-19 Vaccination Process

In this paper, a decision support system (DSS) is presented that focuses on the capacity planning of the COVID-19 vaccination process in the Netherlands. With the Dutch national vaccination priority list as the starting point, the DSS aims to minimize the per-class waiting-time with respect to (1) the locations of the medical hubs (i.e., the vaccination locations) and (2) the distribution of the available vaccines and healthcare professionals (over time). As the user is given the freedom to experiment with different starting positions and strategies, the DSS is ideally suited for providing support in the dynamic environment of the COVID-19 vaccination process. In addition to the DSS, a mathematical model to support the assignment of inhabitants to medical hubs is presented. This model has been satisfactorily implemented in practice in close collaboration with the Dutch Municipal and Regional Health Service (GGD GHOR Nederland).