Miracle-dz/UniFAR-FTUs huggingface dataset:
A framework for learning by demonstration in multi-teacher multi-robot scenarios

As robots become more accessible to humans, more intuitive and human-friendly ways of programming them with interactive and group-aware behaviours are needed. This thesis addresses the gap between Learning by Demonstration and Multi-robot systems. In particular, this thesis tackles the fundamental problem of learning multi-robot cooperative behaviour from concurrent multi-teacher demonstrations, problem which had not been addressed prior to this work. The core contribution of this thesis is the design and implementation of a novel, multilayered framework for multi-robot learning from simultaneous demonstrations, capable of deriving control policies at two different levels of abstraction. The lower level learns models of joint-actions at trajectory level, adapting such models to new scenarios via feature mapping. The higher level extracts the structure of cooperative tasks at symbolic level, generating a sequence of robot actions composing multi-robot plans. To the best of the author’s knowledge, the proposed framework is the first Learning by Demonstration system to enable multiple human demonstrators to simultaneously teach group behaviour to multiple robots learners. A series of experimental tests were conducted using real robots in a real human workspace environment. The results obtained from a comprehensive comparison confirm the applicability of the joint-action model adaptation method utilised. What is more, the results of several trials provide evidence that the proposed framework effectively extracts reasonable multi-robot plans from demonstrations. In addition, a case study of the impact of human communication when using the proposed framework was conducted, suggesting
Security in Untrusted Updated Environments

At present days, technology growth was rapid and its use is very often. So the attacks were concentrated on the user systems mainly by using the network applications. Bugs in the application of a network can ruin the applications in a system that are running. When the user is in the use of internet or e-commerce sites, etc., the applications will be considered that they are in an unsafe environment. Providing security to the network applications like web servers, mails, etc. Is very difficult because they are usually very big applications to make them free from bugs. Now this paper describes how to provide security to the network applications which are in unsafe environment. This idea describes that all the applications were wrapped together for the security purpose and there will be no use to rewrite the network applications.
Probing the Origin of the Open Circuit Voltage in Perovskite Quantum Dot Photovoltaics

Perovskite quantum dots (PQDs) have many properties that make them attractive for optoelectronic applications, including expanded compositional tunability and crystallographic stabilization. While they have not achieved the same photovoltaic (PV) efficiencies of top-performing perovskite thin films, they do reproducibly show high open circuit voltage (VOC) in comparison. Further understanding of the VOC attainable in PQDs as a function of surface passivation, contact layers, and PQD composition will further progress the field and may lend useful lessons for non-QD perovskite solar cells. Here, we use photoluminescence-based spectroscopic techniques to understand and identify the governing physics of the VOC in CsPbI3 PQDs. In particular, we probe the effect of the ligand exchange and contact interfaces on the VOC and free charge carrier concentration. The free charge carrier concentration is orders of magnitude higher than in typical perovskite thin films and could be tunable through ligand chemistry. Tuning the PQD A-site cation composition via replacement of Cs+ with FA+ maintains the background carrier concentration but reduces the trap density by up to a factor of 40, reducing the VOC deficit. These results dictate how to improve PQD optoelectronic properties and PV device performance and explain the reduced interfacial recombination observed by coupling PQDs with thin-film perovskites for a hybrid absorber layer.
Extraction, Estimation and Thin Layer Chromatography of Saponins: A Review

Since time immemorial saponins are in use in various parts of the world for sure shot treatment or for approximate and rudimentary treatment of various ailments. Till date they are among the most fancied natural chemical for scientist of various backgrounds (botanist, biochemist, pharmacognosist, pharmacologist, natural product chemist etc), because they are one of the most potent and promising bioactive chemicals, obtained from natural sources. Various saponins have been reported in literature for range of pharmacological activities. Worldwide drug discovery is going through a rough patch. Various models are being discussed, new approaches are emerging, and synthetic drugs are enjoying good position among available options, still natural sources can never be undermined, as most of the established drug molecules trace their route to natural sources directly or indirectly. Many naturally available saponins are being used in treatment of diseases. In this review article an attempt has been made to compile basic and crucial information (extraction methods, thin layer chromatography (TLC) and estimation) on putative secondary metabolite, saponins. Compiled information is highly concise and widely reported.
Review of advances in micromechanical modeling of aggregate–aggregate interactions in asphalt mixtures

This paper presents a comprehensive review of the work done by a number of researchers on the modeling of asphalt mixture. Included are some of the earliest models such as those with non-interacting particles (models with and without geometry specified), models with particle interaction, and some new models developed in recent years. The paper focuses on the description and comparison of the most recently developed finite element network model (FENM), a clustered discrete element model (DEM), and a micromechanical finite element model (FEM) used in micromechanical modeling of asphalt mixture. These models consider the complex mixture microstructure and aggregate–aggregate interaction. These models are demonstrated and applications of the advances are provided, where virtual laboratory simulation and laboratory tests were employed. The feasibility of nanotechnology application in asphalt mixture is also briefly discussed.Key words: micromechanical modeling, micromechanics, aggregate–aggregate interaction, .
Condition Evaluation of Safety Critical Shut-Down Valves

This article gives an overview on how safety critical shutdown valves can be monitored and condition evaluated. These types of valves are the last barrier in maintaining the safety of personnel and environment, hence they must always function as required. Safety critical valves can be found throughout the whole petro-chemical industry, offshore topside, sub-sea and onshore, in a wide variety of sizes. To detect internal leaks in a closed valve, the operator can choose between remote monitoring, do intrusive manual inspection or non-intrusive inspection. Some methods can even be done without shutting down the system, and there are several other advantages and disadvantages that must be considered. The person in charge of the plant valves needs to have an overview to help make sure the reliability of the valves is as good as possible, but without inflicting too much down-time and inspection cost related to maintenance. Furthermore, methods for valve internal leak quantification estimation are reviewed. And finally, the presented maintenance methods are compared and discussed so that recommendations for the best possible scheme can be made.Copyright © 2009 by ASME
Downlink Frequency Reuse and Hopping for OFDMA Femtocells

To prevent the Femto-to-Macro and Femto-to-Femto inter-cell interference, it is essential for the Femto Base Stations to perform interference management in its downlink. This paper exploits the distinct characteristics of OFDMA Femtocells and reveals the simple but effective mechanics for Femto BSs to mitigate the inter-cell interference when Macro BSs are performing fractional frequency reuse. The idea consists of frequency pattern counter-reusing and band-wide frequency hopping. Base on this idea, we then proposed a heuristic Frequency Reuse and Hopping scheme and the later simulation results proved that it can satisfactorily improve the system performance. By following the routines of low-cost low-complexity in design, the proposed scheme is grounded on realistic assumptions and easy to implement. Index Terms – OFDMA, Femtocell downlink, Frequency Reuse, Frequency Hopping
Nutritional Composition, Techno-Functional Properties and Sensory Analysis of Pan Bread Fortified with Kenaf Seeds Dietary Fibre

Kenaf seeds are underutilized source of food with good source of dietary fiber, protein, essential oil, and phytocompounds. The objectives of this study were to determine the nutritional composition of kenaf seeds, the techno-functional properties of kenaf seeds dietary fibre (KSDF), and sensory analysis of pan bread fortified with dietary fibre that was extracted from kenaf seeds. Analyses showed that kenaf seeds are rich in dietary fibre (28.87 g/ 100 g), protein (27.07 g/ 100 g), oil (23.78 g/100 g) and mineral (5.55 g/100 g). The dietary fibre that was extracted through enzymatic hydrolysis (KSDF (EH)) exhibited significantly (p < 0.05) greater water-binding capacity (WBC), oil-binding capacity (OBC) and viscosity than non-enzymatic hydrolyzed kenaf seeds dietary fibre (KSDF (NEH)) and defatted kenaf seed meal (DKSM). Different formulations of bread were prepared by replacing 10% of wheat flour with wheat bran fibre (positive control), rice bran fibre and KSDF, with white bread unfortified with fibre as negative control. Addition of 10% KSDF to bread formulation significantly (p < 0.05) reduced bread height, volume, specific volume, water activity and firmness, and increased proofing time and bread surface colour. Results from the sensory evaluation of the bread samples also showed that KSDF bread was the most acceptable in comparison to rice bran and wheat bran fortified breads. This study shows that kenaf seed has valuable source of dietary fibre with the potential to be used as a functional ingredient in the development of functional breads.
Non-Linear State Estimation Using Derivative-Free Filters for a Three-Phase Induction Motor Model

Abstract This paper is intended to generate unbiased state estimates of a three-phase induction motor using derivative-free state estimation algorithms. The use of intrusive sensors located within the air-gap of the machine has typically been plagued by complexities and lack of robustness. Moreover, any eventual damage in the sensor results in the substitution of the whole motor. On the other hand, there have been major efforts to reduce the cost of high performance systems, besides increasing the versatility of the motor that all necessitate the use of state observers, thereby eliminating the sensors and their interfaces. In this work, a comparative analysis of the three derivative-free non-linear filtering schemes to estimate the states of a three-phase induction motor on the simulated model is presented. The efficacy of ensemble Kalman filter (EnKF) against the traditional sampling importance re-sampling particle filter (SIR-PF) and unscented Kalman filter (UKF) is illustrated. Comparative Monte Carlo simulation results are investigated comprehensively with respect to three different scenarios, namely step changes in load torque, speed reversal, and low speed operation. Computer simulations have been carried out in the presence of additive state and measurement uncertainties and from the extensive simulation studies, it is being inferred that the SIR-PF fails to generate accurate state estimates in the presence of step changes in the load torque as it does not take into account the most current observation in the sampling stage, whereas EnKF and UKF work well. Moreover, when compared on the basis of the sum of squares of the estimation errors (SSEE), which is very often used as the performance index, the performance of the unconstrained estimation algorithm using the EnKF is found to be significantly better than those obtained using UKF and SIR-PF formulations for all the scenarios taken for the simulation study. The results throw light on alleviating the intrinsic intricacies encountered in SIR-PF in parlance with the observer theory.
Diversified intrusion detection using Various Detection methodologies with sensor fusion

Various Intrusion Detection System (IDS) in the literature have shown that multiple classifier may be well versed in detecting the specific attack, but detecting different types of attack is low. In order to ensure high security this work focuses on multiple classifier fusion technique to increase detection rate. The primary role of classifier is to classify the correct and incorrect instance therefore multiple classifier design that is practical, and detects more attack by means of combining them is preferred here. To our best knowledge, this is the first design that considers multiple classifier in which all classifiers are different that detects both anomalies based and misuse based attacks. The dataset collected in a networking environment with the relatively high data density may contain attacks that assaults the system and thus violates system security. In this paper the operation of combining multiple classifiers that detects all categories of attack, from that improving the detection rate and true positive rate thereby reducing the false positive rate can be done. Decision based on threshold value and combining the classifiers result based on majority voting rule helps to increase the overall efficiency and accuracy in detecting the various categories of attack.
COVID-19 Vaccine Does Not Increase the Risk of Disease Flare-Ups among Patients with Autoimmune and Immune-Mediated Diseases

Background: Reports describing post-vaccine autoimmune phenomena, in previously healthy individuals, increased the concerns regarding the risk of disease flare-ups in patients with immune diseases. We aimed to assess the potential risk of disease flare-up, after receiving the COVID-19 (Coronavirus disease 2019) vaccine, during a follow-up period of 6 months. Methods: We performed a prospective cohort study, enrolling the patients with autoimmune- and immune-mediated diseases who voluntarily completed our questionnaire, both online and during hospital evaluations. Based on their decision to receive the vaccine, the patients were divided into two groups (vaccinated and non-vaccinated). Participants who chose not to receive the vaccine served as a control group in terms of flare-ups. Results: A total of 623 patients, 416 vaccinated and 207 non-vaccinated, were included in the study during hospital evaluations (222/623) and after online (401/623) enrolment. There was no difference concerning the risk of flare-up between vaccinated and non-vaccinated patients (1.16, versus 1.72 flare-ups/100 patients-months, p = 0.245). The flare-ups were associated with having more than one immune disease, and with a previous flare-up during the past year. Conclusions: We did not find an increased risk of flare-up following COVID-19 vaccination in patients with autoimmune-/immune-mediated diseases, after a median follow-up of 5.9 months. According to our results, there should not be an obvious reason for vaccine hesitancy among this category of patients.
Examining the Drivers of Racial/Ethnic Disparities in Non-Adherence to Antihypertensive Medications and Mortality Due to Heart Disease and Stroke: A County-Level Analysis

Background: Prior research has identified disparities in anti-hypertensive medication (AHM) non-adherence between Black/African Americans (BAAs) and non-Hispanic Whites (nHWs) but the role of determinants of health in these gaps is unclear. Non-adherence to AHM may be associated with increased mortality (due to heart disease and stroke) and the extent to which such associations are modified by contextual determinants of health may inform future interventions. Methods: We linked the Centers for Disease Control and Prevention (CDC) Atlas of Heart Disease and Stroke (2014–2016) and the 2016 County Health Ranking (CHR) dataset to investigate the associations between AHM non-adherence, mortality, and determinants of health. A proportion of days covered (PDC) with AHM < 80%, was considered as non-adherence. We computed the prevalence rate ratio (PRR)—the ratio of the prevalence among BAAs to that among nHWs—as an index of BAA–nHW disparity. Hierarchical linear models (HLM) were used to assess the role of four pre-defined determinants of health domains—health behaviors, clinical care, social and economic and physical environment—as contributors to BAA–nHW disparities in AHM non-adherence. A Bayesian paradigm framework was used to quantify the associations between AHM non-adherence and mortality (heart disease and stroke) and to assess whether the determinants of health factors moderated these associations. Results: Overall, BAAs were significantly more likely to be non-adherent: PRR = 1.37, 95% Confidence Interval (CI):1.36, 1.37. The four county-level constructs of determinants of health accounted for 24% of the BAA-nHW variation in AHM non-adherence. The clinical care (β = −0.21, p < 0.001) and social and economic (β = −0.11, p < 0.01) domains were significantly inversely associated with the observed BAA–nHW disparity. AHM non-adherence was associated with both heart disease and stroke mortality among both BAAs and nHWs. We observed that the determinants of health, specifically clinical care and physical environment domains, moderated the effects of AHM non-adherence on heart disease mortality among BAAs but not among nHWs. For the AHM non-adherence-stroke mortality association, the determinants of health did not moderate this association among BAAs; the social and economic domain did moderate this association among nHWs. Conclusions: The socioeconomic, clinical care and physical environmental attributes of the places that patients live are significant contributors to BAA–nHW disparities in AHM non-adherence and mortality due to heart diseases and stroke.
A balanced dual-diaphragm resonant pressure sensor in silicon

The design and fabrication of a resonant differential pressure sensor is described. The vibrating part consists of a dual-diaphragm structure suspended inside a frame. Anisotropic silicon etching and thermal silicon bonding techniques are used to fabricate the device. The sensor is electrostatically excited to vibration in a torsional balanced mode which yields a high Q factor of 2400 in air. The pressure sensitivity is based on a torsional stiffness change of the vibrating element. Measurements show a pressure sensitivity of 19%/bar over the range -0.5 to +0.5 bar and a very low temperature sensitivity of the resonance frequency of -16 p.p.m. / degrees C. >
Inhalation Rates for Risk Assessments Involving Construction Workers in Canada

ABSTRACT A Monte Carlo simulation was performed to generate probability density distributions describing hourly inhalation rates of male and female construction workers. Ranges of oxygen uptake and ventilation rates reported in the literature for various construction trades were manipulated in a simulation to generate hourly inhalation rates for male construction workers. The resulting inhalation rates can be represented by a log normal distribution with mean ± standard deviation of 1.40 ± 0.51 m3/h. Hourly inhalation rates for female construction workers were scaled from those of their male counterparts using relative awake-time inhalation rates of men and women of the general public. Female construction workers' inhalation rates can be represented by a log normal distribution with a mean ± standard deviation of 1.25 ± 0.66 m3/h. If the nature of the work is well understood, male and female construction workers' hourly inhalation rates can be scaled according to the construction trade. Construction trade-specific scaling factors were developed and range from 0.78 for electricians to 1.11 for ironworkers.
Fault Detection of Wind Turbine Generator Bearing Using Attention-Based Neural Networks and Voting-Based Strategy

Wind turbines are usually exposed to dynamic and adverse environmental conditions, and faults on mechanical parts are major threats to the economic efficiency of wind farms. Condition monitoring and fault detection play important roles in the reliability of wind turbines. However, due to the dynamic characteristics and fluctuations of variables, precise modeling for representing the complex operation state of key components is still challenging. This study presents a data-driven fault detection method for generator bearing of wind turbines. A dual-stage attention-based recurrent neural network is first trained to model the normal behavior of generator bearing temperature. Based on long short-term memory, an input attention mechanism is specially designed to allocate the variable importances specific to each timeslot. Since the variable importance is distinct at different timeslots, the time-varying correlations among variables in wind turbines can be captured, and hence, the performance of variable estimation can be remarkably improved. In addition, this article focuses on the challenges faced by the practice of wind power industries, where the evolution rates of impending faults are usually unknown and distinct, and proposes a novel voting-based detection strategy. The proposed strategy conducts multiple processes to evaluate a potential fault from different perspectives and, hence, can guarantee the robustness and performance of fault detecting. Eventually, field supervisory control and data acquisition data from real wind farms are utilized for validation and extensive comparison studies verify the effectiveness of this proposed scheme.
Route guidance with unspecified staging posts using genetic algorithm for car navigation systems

This paper addresses the problem of selecting a route to a given destination that traverses several nonspecific sites (e.g. 'a bank') as requested by a driver. The proposed solution uses a genetic algorithm that includes viral infection. A road that includes a site is regarded as a virus. An infection occurs between two points common to a candidate route and the virus, and involves the substitution of the intersections carried by the virus for those on the existing candidate route. Crossover and infection determine the easiest-to-drive and quasi-shortest route through the objective landmarks. Experiments using actual road maps show that this infection-based mechanism is an effective way of solving the problem.