Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust H∞ Observer-based Reference Tracking Control Design of Nonlinear Stochastic Systems : HJIE-embedded Deep Learning Approach

The robust H∞ observer-based reference tracking control design of nonlinear stochastic systems with external disturbance and measurement noise is always a very complicated and difficult problem in the control field. It needs to solve a very difficult control-observer-coupled Hamilton Jacobi Isaacs equation (HJIE) for nonlinear observer and controller in the design procedure. At present, there exists no analytic and numerical way for solving this control-observer-coupled HJIE. A novel HJIE-embedded deep learning approach is proposed as a co-design of deep learning algorithm and H∞ observer-based tracking control scheme to directly solve the nonlinear partial differential control-observer-coupled HJIE of H∞ observer-based reference tracking control design problem of nonlinear stochastic systems. In the off-line training phase, state estimation error and tracking error are inputed to HJIE-embedded deep neural network (DNN) to output the solution of HJIE. If not, the learning error of HJIE is fedback to train DNN to solve HJIE for H∞ tracking control law, observer gain as well as the worst-case external disturbance and measurement noise, which will be sent back to nonlinear stochastic system model to replace the external disturbance and measurement noise and estimation error signal for next step training. The proposed DNN-embedded H∞ observer-based reference tracking scheme can achieve the theoretical H∞ observer-based reference tracking control strategy as the deep learning algorithm converges. If free of external disturbance and measurement noise, the proposed DNN-based H∞ observer-based reference tracking control scheme can approach to the stochastically asymptotical state estimation and reference tracking simultaneously. Finally, a design example of H∞ observer-based reference tracking control for quadrotor UAV system with external disturbance and output measurement noise is provided to illustrate the design procedure and to validate the state estimation and reference tracking performance simultaneously of the proposed HJIE-embedded H∞ DNN-based observer-based reference tracking control scheme of nonlinear stochastic systems.
Micro gimbal diaphragm for sound source localization with mimicking Ormia Ochracea

The parasitoid fly Ormia Ochracea shows a remarkable ability to localize a sound source although the interaural differences in intensity and time are extremely small. In this paper, mimicking unique structures of its auditory organ, we propose a micro sound source localization sensor using a gimbal diaphragm, which realizes a simple and optimal transduction mechanism for detecting a tiny pressure gradient. We made a prototype from a thin metal plate and confirmed that it works as a sound source localization sensor by experiments.
Estimating Reservoir Inflow and Outflow From Water Level Observations Using Expert Knowledge: Dealing With an Ill‐Posed Water Balance Equation in Reservoir Management

Quantifying reservoir water balance is an essential process for the efficient management of water resources. Water level records are often the only data available for reservoir analysis, which causes an ill‐posed problem in water resource system planning. This study proposes an analytical framework to estimate reservoir inflow and outflow from water level observations using hydrological models and reasoning carefully derived from expert knowledge and soft data. Partial reservoir inflow hydrographs were constructed from water level observations using a continuity equation and knowledge‐based constraints developed for periods of no spillway discharge. A bucket‐type rainfall‐runoff model was then calibrated to the partial inflow hydrographs. Finally, a complete reservoir inflow hydrograph was constructed using the calibrated models, which were then employed to estimate detailed reservoir outflow components under a full water balance relationship between inflow, outflow, water levels, and reservoir operation rules. The proposed solution for the ill‐posed water balance equation outperformed conventional (benchmark) approaches in accuracy and uncertainty in its application to agricultural reservoirs. This study demonstrates how hydrological modeling and reasoning, which are discreetly designed based on expert knowledge, can help to solve the ill‐posed water balance equation by creating supplementary information regarding the problem. The proposed framework is expected to assist in reconstructing reservoir routing processes using water level observations for hydrological analysis and water resource management planning.
Mathematical Modeling and Stability Analysis of Tiltrotor Aircraft

The key problem in the development process of a tiltrotor is its mathematical modeling. Regarding that, this paper proposes a dividing modeling method which divides a tiltrotor into five parts (rotor, wing, fuselage, horizontal tail, and vertical fin) and to develop aerodynamic models for each of them. In that way, force and moment generated by each part are obtained. Then by blade element theory, we develop the rotor’s dynamic model and rotor flapping angle expression; by mature lifting line theory, the build dynamic models of the wings, fuselage, horizontal tail and vertical fin and the rotors’ dynamic interference on wings, as well as nacelle tilt’s variation against center of gravity and moment of inertia, are taken into account. In MATLAB/Simulink simulation environment, a non-linear tiltrotor simulation model is built, Trim command is applied to trim the tiltrotor, and the XV-15 tiltrotor is taken as an example to validate rationality of the model developed. In the end, the non-linear simulation model is linearized to obtain a state-space matrix, and thus the stability analysis of the tiltrotor is performed.
Simultaneous Determination of Content and Antioxidant Activity of Five Components in Hibiscus mutabilis L by HPLC-QAMS

Based on high performance liquid chromatography (HPLC), a method for simultaneous determination of five active components in Hibiscus mutabilis L was established by quantitative analysis of multicomponents by single marker (QAMS). This method was used to evaluate the quality of Hibiscus mutabilis L. In the study, quercetin was used as the internal standard, and the relative correction factors (RCF) of rutin, protocatechuic acid, catechin, and luteolin were calculated, and the contents of five components were determined simultaneously by quercetin. Compared with the traditional external standard method (ESM), this method had less error and higher feasibility, and the methodological experiments showed that the five components had a great linear relationship within their respective concentration ranges ( r ≥ 0.9995 ). The average recovery was 96.97%-98.85% (RSD was 0.88%-1.81%), precision ( RSD ≦ 1.83 % ), repeatability ( RSD ≦ 1.87 % ), and stability ( RSD ≦ 1.51 % ) were great. In this experiment, the contents of these five active components in Hibiscus mutabilis L from five producing areas were determined. Finally, the antioxidant capacity of Hibiscus mutabilis L was determined to determine its antioxidant activity.
Deep-Learning-Based Automatic Mineral Grain Segmentation and Recognition

A multitude of applications in engineering, ore processing, mineral exploration, and environmental science require grain recognition and the counting of minerals. Typically, this task is performed manually with the drawback of monopolizing both time and resources. Moreover, it requires highly trained personnel with a wealth of knowledge and equipment, such as scanning electron microscopes and optical microscopes. Advances in machine learning and deep learning make it possible to envision the automation of many complex tasks in various fields of science at an accuracy equal to human performance, thereby, avoiding placing human resources into tedious and repetitive tasks, improving time efficiency, and lowering costs. Here, we develop deep-learning algorithms to automate the recognition of minerals directly from the grains captured from optical microscopes. Building upon our previous work and applying state-of-the-art technology, we modify a superpixel segmentation method to prepare data for the deep-learning algorithms. We compare two residual network architectures (ResNet 1 and ResNet 2) for the classification and identification processes. We achieve a validation accuracy of 90.5% using the ResNet 2 architecture with 47 layers. Our approach produces an effective application of deep learning to automate mineral recognition and counting from grains while also achieving a better recognition rate than reported thus far in the literature for this process and other well-known, deep-learning-based models, including AlexNet, GoogleNet, and LeNet.
A Latent Class Analysis of the Antisocial Attitudes Domain of the Youth Level of Service/Case Management Inventory

Antisocial attitudes are a strong predictor of reoffending and frequently incorporated into risk assessment tools, including the Youth Level of Service/Case Management Inventory (YLS/CMI). However, YLS/CMI Attitudes/Orientation domain items appear to cover different issues—antisocial attitudes and willingness to engage in treatment—which have different implications for case management and service provision. Latent Class Analysis of data from 798 Canadian youth probationers identified four classes based on item endorsement on the Attitudes/Orientation domain: High Overall Attitude Needs (19%), Predominantly Antisocial Attitude Items (20%), Predominantly Lack of Service Engagement (9%), and Low Overall Attitude Needs (52%). Class differences were found on index offense, criminogenic needs, and recidivism, with the High Overall Attitude Needs class presenting as most “negative,” followed by Predominantly Antisocial Attitude Items, Predominantly Lack of Service Engagement, and Low Overall. Understanding attitudes based on this class conceptualization can assist probation officers in targeting services more effectively to justice-involved youth.
Accelerators, startup performance, and crises

New accelerator programs have developed globally over the last decade. The accelerator concept is widely discussed in the media and receives increasing interest from researchers. However, the performance and impact of accelerators is often debated. This paper's objective is to offer a qualitative overview of different accelerator types and empirical analysis of the performance of two popular accelerators: Y Combinator and Techstars. Based on characteristics of the accelerators and the economic environment, this paper presents a first attempt to understand the influence of economic shocks on accelerated startups in the context of the recent Great Recession (2007-2009). Three core findings were identified in this research. First, the death rate of startups accelerated during the recession is significantly greater than of startups accelerated before or after the recession. This finding questions the added value of attending an accelerator program during a recession and calls for crisis-specific initiatives to increase the resilience of the accelerated startups. Second, the scarce VC funding in a location during the crisis is associated with the increased death rates. Third, the two accelerators seem to have changed their business model, increasing the number of later stage startups (i.e., those with prior funding) accepted in their programs.
Maximum Power Point Tracking of Photovoltaic Systems Based on Fast Terminal Sliding Mode Controller

This work introduces a maximum power point tracking method based on fast terminal sliding mode control (FTSMC) for photovoltaic systems. The control scheme contains two loops: the first achieves the maximum power point (MPP) searching via an algorithm which gives the reference voltage (MPV). The second loop achieves regulation of the operating point to the reference MPV issued from the first loop. Unlikely to the traditional sliding mode, the FTSM controller guarantees finite time convergence of the MPPT with robustness. Control performances are proved throw several simulations.
Insights Gained from Analysis of Performance and Participation in a Flipped Classroom

A flipped classroom uses technology to move lectures outside the classroom reserving the time inside the classroom for learning activities that connect concepts with practice. There has been limited research looking at student behaviours in a flipped learning environment and the extent to which students actually prepare for the face to face workshop experience as anticipated in the flipped learning model. Data collected from the learning management system (LMS) is used to examine the relationship between participation in pre-workshop activities and performance in a flipped learning class. The study cohort is a large class of final year bachelor and master of engineering students studying Risk, Reliability and Safety. Results show that participation associated with looking at pre-workshop materials and engaging in discussion boards is positively related to performance in summative assessments. There are lower levels of participation by international students in pre-work and discussion boards compared to their domestic counterparts; this may be related to language and the need to improve 'engineering business' skills. This work shows how technology, in the form of the data on participation collected in the LMS, can also be used to examine student behaviour, inform flipped learning teaching practice and identify future research questions.
User Orientation Maps: An Approach to Address User Diversity in Design for Sustainable Behaviour

Abstract There has been an increasing tendency among design researchers to explore the potential of design to change user behaviour in the interest of sustainability. To date, numerous techniques have been proposed, aimed at providing more guidance to design researchers and practitioners on how to encourage sustainable behaviour. Choosing a technique suitable for both the target behaviour and the target user is a critical task when designing for sustainable behaviours. The influence of individual characteristics on user compliance with different techniques makes this task more compelling for designers. To make this task easier for them, we offer a new approach, called the ‘user orientation map’, to explore and represent user diversity by showing the range of user types and techniques to promote behavioural change for each user type. We illustrate this approach with a case study on eco-friendly driving, and identify nine user types.
Thiolated Chitosan Conjugated Liposomes for Oral Delivery of Selenium Nanoparticles

This study aimed to design a hybrid oral liposomal delivery system for selenium nanoparticles (Lip-SeNPs) to improve the bioavailability of selenium. Thiolated chitosan, a multifunctional polymer with mucoadhesive properties, was used for surface functionalization of Lip-SeNPs. Selenium nanoparticle (SeNP)-loaded liposomes were manufactured by a single step microfluidics-assisted chemical reduction and assembling process. Subsequently, chitosan-N-acetylcysteine was covalently conjugated to the preformed Lip-SeNPs. The Lip-SeNPs were characterized in terms of composition, morphology, size, zeta potential, lipid organization, loading efficiency and radical scavenging activity. A co-culture system (Caco-2:HT29-MTX) that integrates mucus secreting and enterocyte-like cell types was used as a model of the human intestinal epithelium to determine adsorption, mucus penetration, release and transport properties of Lip-SeNPs in vitro. Thiolated Lip-SeNPs were positively charged with an average size of about 250 nm. Thiolated Lip-SeNPs tightly adhered to the mucus layer without penetrating the enterocytes. This finding was consistent with ex vivo adsorption studies using freshly excised porcine small intestinal tissues. Due to the improved mucoadhesion and retention in a simulated microenvironment of the small intestine, thiolated Lip-SeNPs might be a promising tool for oral selenium delivery.
Adaptive Intelligent Sliding Mode Control of a Dynamic System with a Long Short-Term Memory Structure

In this work, a novel fuzzy neural network (NFNN) with a long short-term memory (LSTM) structure was derived and an adaptive sliding mode controller, using NFNN (ASMC-NFNN), was developed for a class of nonlinear systems. Aimed at the unknown uncertainties in nonlinear systems, an NFNN was designed to estimate unknown uncertainties, which combined the advantages of fuzzy systems and neural networks, and also introduced a special LSTM recursive structure. The special three gating units in the LSTM structure enabled it to have selective forgetting and memory mechanisms, which could make full use of historical information, and have a stronger ability to learn and estimate unknown uncertainties than general recurrent neural networks. The Lyapunov stability rule guaranteed the parameter convergence of the neural network and system stability. Finally, research into a simulation of an active power filter system showed that the proposed new algorithm had better static and dynamic properties and robustness compared with a sliding controller that uses a recurrent fuzzy neural network (RFNN).
Co-construction and sharing of a Chinese medical case repository helps medical workers strengthen capacity for clinical practices

The advent of the era of big data is bound to promote the development of databases. How to bring clinicians’ focus back to clinical practice and enhance the clinical capabilities of general physicians at the primary level is a question that every member of the academic publishing profession needs to consider. If data and knowledge can be shared openly, research and medical management will develop at an accelerated pace. Given the global consensus on open science, the co-construction and sharing of a Chinese medical case repository will surely improve the practical capability of clinicians.
Optimizing Polynomial-Time Solutions to a Network Weighted Vertex Cover Game

Weighted vertex cover (WVC) is one of the most important combinatorial optimization problems. In this paper, we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted networks. We first model the WVC problem as a general game on weighted networks. Under the framework of a game, we newly define several cover states to describe the WVC problem. Moreover, we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums (SNEs) of the game. Then, we propose a game-based asynchronous algorithm (GAA), which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial time. Subsequently, we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms, termed the improved game-based asynchronous algorithm (IGAA), in which we prove that it can obtain a better solution to the WVC problem than using a the GAA. Finally, numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.
Mucoadhesive PLGA Nanospheres and Nanocapsules for Lactoferrin Controlled Ocular Delivery

Background: the present work describes the preparation, characterization and optimization of eight types of PLGA-based nanosystems (nanospheres and nanocapsules) as innovative mucoadhesive drug delivery systems of lactoferrin, in order to achieve a preclinical consistent base as an alternative pharmacological treatment to different ocular syndromes and diseases. Methods: All different nanoparticles were prepared via two modified nanoprecipitation techniques, using a three-component mixture of drug/polymer/surfactant (Lf/PLGA/Poloxamer), as a way to overcome the inherent limitations of conventional PLGA NPs. These modified polymeric nanocarriers, intended for topical ophthalmic administration, were subjected to in vitro characterization, surface modification and in vitro and in vivo assessments. Results: An appropriate size range, uniform size distribution and negative ζ potential values were obtained for all types of formulations. Lactoferrin could be effectively included into all types of nanoparticles with appropriate encapsulation efficiency and loading capacity values. A greater, extended, and controlled delivery of Lf from the polymeric matrix was observed through the in vitro release studies. No instability or cytotoxicity was proved for all the formulations by means of organotypic models. Additionally, mucoadhesive in vitro and in vivo experiments show a significant increase in the residence time of the nanoparticles in the eye surface. Conclusions: all types of prepared PLGA nanoparticles might be a potential alternative for the topical ophthalmic administration of lactoferrin.
Texture-Based Woven Image Classification using Fuzzy C-Means Algorithm

—There are a lot of texture-based image data stored in the storage media Internet. Most of these data portray the cultural fabric texture results from a State. Because of the many variants of the existing texture, the data need to be easily accessible through the Internet. Moreover, the area of origin of weaving the surface is easily known. Therefore, it is necessary to develop a classification system based on woven image data. The texture of the image data stored in a database on the Internet can be grouped/clustered well, making it easy to access. This study examines a texture-based woven image classification using fuzzy c-means algorithm. This method combines extraction meth- ods Gabor filter, fuzzy c-means algorithm and Euclid distance similarity measure. An experiment was done using the system as many as 60 woven images from Bali, NTT and Central Java areas, each taken as many as 25 images weaving. The test results stated that testing using the test images taken from the images in the database generates a 100% accuracy rate, and testing using test images taken from outside the database produces an accuracy rate of 94%.
Measuring the Impact of DoS Attack on Availability: Empirical Study Based on Accessibility

Information Security is determined by three well know security parameters i.e. Confidentiality, Integrity and Availability. Availability is an important pillar when it comes to security of an information system. It is dependent upon the reliability, timeliness and accessibility of the Information System. This paper presents an analytical view of the fact that when Accessibility is degraded during the presence of an ongoing attack, the other factors reliability and timeliness can also get affected, therefore creating a degrading impact on the overall Availability of the system, which eventually leads to the Denial of Service Attack and therefore affecting the security of the System.