Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictor-based output feedback control design for sampled systems with input delay subject to disturbance

In this study, a predictor-based output feedback control design is proposed for sampled systems with input delay subject to disturbance. An extended state observer (ESO) is first constructed to simultaneously estimate the system state and disturbance based on only the output measurement. Then a filtered predictor is constructed by using the estimated state and disturbance to compensate the input delay so as to improve the disturbance rejection performance. For the presence of a constant or asymptotically stable disturbance, a robust H infinity feedback control design based on state prediction and disturbance estimation is proposed to realise no steady-state output error, despite mismatched disturbance. When there exists a time-varying disturbance with deterministic dynamics, such as a sinusoidal type, the proposed predictor can achieve a small prediction error by properly tuning the filter, such that the existing observer-based feedback control methods for delay-free systems can be directly adopted to stabilise such a system with input delay. Moreover, the output error bounds in the presence of time-varying disturbance are quantitatively analysed for using the ESO-based predictor feedback and anti-disturbance predictor feedback, respectively. Two illustrative examples are given to demonstrate the effectiveness and merit of the proposed method.
Effects of working pressure and annealing on bulk density and nanopore structures in amorphous In–Ga–Zn–O thin-film transistors

Microstructures of amorphous In–Ga–Zn–O (a-IGZO) thin films of different densities were analyzed. Device-quality a-IGZO films were deposited under optimum conditions, e.g., the total pressure Ptot = 0.55 Pa produced high film densities of ∼6.1 g/cm3, while a very high Ptot = 5.0 Pa produced low film densities of 5.5 g/cm3. Both films formed uniform high-density layers in the vicinity of the glass substrate, 10–20 nm in thickness depending on Ptot, while their growth mode changed to a sparse columnar structure in thicker regions. X-ray reflectivity and in situ spectroscopic ellipsometry provided different results on densification by post deposition thermal annealing; i.e., the latter has a higher sensitivity. High-Z-contrast images obtained by high-angle annular dark-field scanning transmission electron microscopy were also useful for detecting nanometer-size non uniformity even in device-quality a-IGZO films.
Freeway Merging Decision Analysis on the Acceleration Lane

Numerous traffic accidents and near-crashes are caused by lane-changing maneuvers. A merging vehicle (MV) on a freeway acceleration lane (AL) has to merge into the faster-traveling mainline stream before the end of the AL. With the space constraint and speed difference pressure, freeway AL merging is more complicated and riskier compared with discretionary lane changes. Adequately characterizing the driving behaviors in different merging scenarios and in different sections of the AL can help understand the mechanism of the merging process and benefit merging decision assistance technology development. To resolve this issue, a data acquisition platform was established to acquire natural merging process data. Statistical analysis results demonstrated that the existence of the leading vehicle on the mainline had no significant effect on the merge position of the MV. The average acceleration or speed of the MV in the “merge” events was relatively larger than that in the “wait” events. As the MV neared the end of the AL, the risk it could bear increased, and the acceptable lag gap threshold and time-to-collision threshold decreased. The main challenge of a merging decision model lies in how to balance the risk acceptance behavior and the spatial constraint of the AL. Based on the analysis, an adaptive boosting (Adaboost) model was established to model the merging decision of the MV considering the current and historical data of the environment and the effect of AL. The overall accuracy of the model turned out to be 98.9%.
Boundary integral equation method for electromagnetic and elastic waves

In this thesis, the boundary integral equation method (BIEM) is studied and applied to electromagnetic and elastic wave problems. First of all, a spectral domain BIEM called the spectral domain approach is employed for full wave analysis of metal strip grating on grounded dielectric slab (MSG-GDS) and microstrips shielded with either perfect electric conductor (PEC) or perfect magnetic conductor (PMC) walls. The modal relations between these structures are revealed by exploring their symmetries. It is derived analytically and validated numerically that all the even and odd modes of the latter two (when they are mirror symmetric) find their correspondence in the modes of metal strip grating on grounded dielectric slab when the phase shift between adjacent two unit cells is 0 or π. Extension to non-symmetric case is also made. Several factors, including frequency, grating period, slab thickness and strip width, are further investigated for their impacts on the effective permittivity of the dominant mode of PEC/PMC shielded microstrips. It is found that the PMC shielded microstrip generally has a larger wave number than the PEC shielded microstrip. Secondly, computational aspects of the layered medim doubly periodic Green’s function (LMDPGF) in matrix-friendly formulation (MFF) are investigated. The MFF for doubly periodic structures in layered medium is derived, and the singularity of the periodic Green’s function when the transverse wave number equals zero in this formulation is analytically extracted. A novel approach is proposed to calculate the LMDPGF, which makes delicate use of several techniques including factorization of the Green’s function, generalized pencil of function (GPOF) method and high order Taylor expansion to derive the high order asymptotic expressions, which are then evaluated by newly derived fast convergent series. This approach exhibits robustness, high accuracy and fast and high order convergence; it also allows fast frequency sweep for calculating Brillouin diagram in eigenvalue problem and for normal incidence in scattering problem. Thirdly, a high order Nyström method is developed for elastodynamic scattering that features a simple local correction scheme due to a careful choice of basis functions. A novel simple and efficient singularity subtraction scheme and a new effective near singularity subtraction scheme are proposed for performing singular and nearly singular
The impact of meteorological conditions on snow and ice thickness in an Arctic lake

Inter-annual variation of meteorological conditions and their effects on snow and ice thickness in an Arctic lake Unari (67.14° N, 25.73° E) were investigated for winters 1980/1981–2012/2013. The lake snow and ice thicknesses were modelled applying a thermodynamic model, and the results were compared with observations. Regression equations were derived for the relationships between meteorological parameters and modelled snow and ice properties. The composite differences of large-scale atmospheric circulation patterns between seasons of thin and thick ice were analysed. The air temperature had an increasing trend (statistical significance p<0.05) during the freezing season (1.0° C/decade), associated with an increasing trend of liquid precipitation (p<0.05) in winter. Both observed and modelled average and maximum ice thicknesses showed a decreasing trend (p<0.05). The model results were statistically more reliable (1) for lake ice than snow and (2) for seasonal means than maxima. Low temperature with less precipitation prompted the formation of columnar ice, whereas strong winds and heavy snowfall were in favour of granular ice formation. The granular (columnar) ice thickness was positively (negatively) correlated with precipitation. The seasonal mean and maximum modelled lake ice and snow thicknesses were controlled by precipitation and temperature history, with 58–86 % of the inter-annual variance explained. Using regression equations derived from data from 1980 to 2013, snow and ice thickness for the following winter seasons was statistically forecasted, yielding errors of 9–12 %. Among large-scale climate indices, the Pacific Decadal Oscillation was the only one that correlated with inter-annual variations in the seasonal average ice thickness in Lake Unari.
High‐Efficiency, High‐Power AlGaInP Thin‐Film LEDs with Micron‐Sized Truncated Cones as Light‐Extraction Structures

Micron‐sized truncated cones with a top surface diameter and a height on the order of the emission wavelength are prepared on the light‐extraction surface of a high‐power thin‐film AlGaInP red LED as the light‐extraction structure. An external quantum efficiency of ≈39.3% is obtained from a 1 × 1 mm2 device after resin encapsulation at an injection current of 120 mA at room temperature (≈25 °C). The light‐extraction process in this device is investigated by means of a finite‐difference time‐domain (FDTD) simulation and is compared with that in a conventional AlGaInP thin‐film LED with a randomly rough texture as the light‐extraction structure. It is found theoretically that the extraction efficiency of light per single surface incidence of the device with the micron‐sized truncated cones is a few tens of percent higher than that of a device with a randomly rough texture on the light‐extraction surface. The combined effect of evanescent wave scattering at facet edges and direct extraction through some specific facets is attributed to be the main mechanism responsible for the enhancement in the light‐extraction efficiency, based on the FDTD simulation.
Integrating ecosystem organization, quality and services indicators in the ecological security assessment

Ecological security is a vital component of regional security and significantly influences regional sustainable development. The assessment of ecological security is the cornerstone of relative research to coordinate ecological protection and economic developments. However, a framework that enables systematic assessment of the ecological security still needs further exploration. A framework that includes ecosystem organization (O), quality (Q), and services (S) was proposed to evaluate the ecological security in southeast Qinghai‐Tibet Plateau (QTP) in this study. Results showed that: (1) From 2000 to 2020, ecosystem organization in the study area showed an improving trend, with 61.33% of counties experiencing an increase in O value; (2) The ecosystem quality showed an increasing trend, with the proportion of increased areas amounting to 54.46%, and the significantly increased area occupied 3.38% of the study area; (3) The total trend of ecosystem services decreased, among them, habitat provision and soil conservation both presented an increasing trend, while water yield performed a decreased trend; (4) The ecological security in the southeast of QTP has been improved, which increased in north of the study area and decreased in south part. Overall, our study provided a novel view to characterize the physical status of the natural environment and enriched the methodology of ecological security assessment. More attention should be received by the different roles of ecosystem organization, quality, and services in ecological security in future studies, which is helpful to reflect the current situation and changing trends of regional ecological security more reasonably and comprehensively.
ENERGY COMPACTION ALGORITHM FOR ENHANCING FOOTPRINT IMAGE QUALITY USING DISCRETE WAVELET TRANSFORMATIONS

A new energy compaction algorithm that compresses the image of a foot print based on Discrete Wavelet Transformation (DWT) using 2-D Haar Wavelet Transform of the gray scale image is proposed. The foot print image is resized to 256 x 256. Wavelet Transforms are used for noise reduction in order to improve the image quality. This is accomplished through Wavelet Decomposition followed by selective compression of undesirable noise components. Use of a Bi-Lateral filter to smoothen the ridges of the foot print and filtering the image without blurring the sharp edges. The reconstruction of the image is done using Inverse Discrete Wavelet Transformation (IDWT) with the maximum correlation of adjacent scales. The image is further used to match identity of the person’s foot print from data base by measuring the Euclidean distance.
Strange attractors in coupled systems

Abstract Following our results in [1] we provide an analytical proof of the existenceof strange attractors in a 4-parameter family of 4-dimensional vector ﬁeldsconsisting of two brusselators (a very well-known model for a chemical reac-tion) linearly coupled by diﬀusion. It is shown that such a family is a genericunfolding of a 4-dimensional nilpotent singularity of codimension 4. On theother hand, we prove that in any generic unfolding X µ of an n-dimensionalnilpotent singularity of codimension n there are bifurcation curves of (n−1)-dimensional nilpotent singularities of codimension n − 1 which are in turngenerically unfolded by X µ . Arguments conclude recalling (see [2] and [3])that any generic unfolding of the 3-dimensional nilpotent singularity of codi-mension 3 exhibits strange attractors since it includes generic unfoldings ofShil’nikov homoclinic orbits. Our theoretical results will be compared withprevious numerical evidences. Discussions will be also included about the useof similar techniques in more general contexts: coupling of more than twooscillators or the case of realistic models but diﬀerent from the brusselator.
Characteristics of Medical Deserts and Approaches to Mitigate Their Health Workforce Issues: A Scoping Review of Empirical Studies in Western Countries

Background: Medical deserts are considered a problematic issue for many Western countries which try to employ multitude of policies and initiatives to achieve a better distribution of their health workforce (HWF). The aim of this study was to systematically map research and provide an overview of definitions, characteristics, contributing factors and approaches to mitigate medical deserts within the European Union (EU)-funded project "ROUTE-HWF" (a Roadmap OUT of mEdical deserts into supportive Health WorkForce initiatives and policies). Methods: We performed a scoping review to identify knowledge clusters/research gaps in the field of medical deserts focusing on HWF issues. Six databases were searched till June 2021. Studies reporting primary research from Western countries on definitions, characteristics, contributing factors, and approaches were included. Two independent reviewers assessed studies for eligibility, extracted data and clustered studies according to the four defined outcomes. Results: Two-hundred and forty studies were included (n=116, 48% Australia/New Zealand; n=105, 44% North America; n=20, 8% Europe). All used observational designs except for five quasi-experimental studies. Studies provided definitions (n=171, 71%), characteristics (n=95, 40%), contributing factors (n=112, 47%), and approaches to mitigate medical deserts (n=87, 36%). Most medical deserts were defined by the density of the population in an area. Contributing factors to HWF issues in medical deserts consisted in work-related (n=55, 23%) and lifestyle-related factors (n=33, 14%) of the HWF as well as sociodemographic characteristics (n=79, 33%). Approaches to mitigate them focused on training adapted to the scope of rural practice (n=67, 28%), HWF distribution (n=3, 1%), support/infrastructure (n=8, 3%) and innovative models of care (n=7, 3%). Conclusion: Our study provides the first scoping review that presents and categorizes definitions, characteristics, contributing factors, and approaches to mitigate HWF issues in medical deserts. We identified gaps such as the scarcity of longitudinal studies to investigate the impact of factors contributing to medical deserts, and interventional studies to evaluate the effectiveness of approaches to mitigate HWF issues.
From urban segregation to spatial structure detection

We develop a ‘multifocal’ approach to reveal spatial dissimilarities in cities, from the most local scale to the metropolitan one. Think, for instance, of a statistical variable that may be measured at different scales, e.g. ethnic group proportions, social housing rate, income distribution, or public transportation network density. Then, to any point in the city there corresponds a sequence of values for the variable, as one zooms out around the starting point, all the way up to the whole city – as if with a varifocal camera lens. The sequences thus produced encode spatial dissimilarities in a precise manner: how much they differ from perfectly random sequences is indeed a signature of the underlying spatial structure. We introduce here a mathematical framework that allows to analyse this signature, and we provide a number of illustrative examples.
What Are Quasirandom Numbers And Are They Good For Anything Besides Integration

Monte Carlo methods are very general methods but suer from the slow, O(N 1=2 ), convergence characteristic of random sampling. In recent years, the convergence of the Monte Carlo method for numerical integration has been improved by replacing pseudorandom numbers (PRNs) with more uniformly distributed numbers known as quasirandom numbers (QRNs). However, the advantages for numerical integration hold for integrals over the unit cube depend on many factors, such as the dimension and smoothness of the integrand, and whether or not the support of the integrand coincides with the unit cube. In the best cases, convergence for numerical integration becomes almost O(N 1 ), while in the worst cases convergence falls back to that obtained with PRNs. In this paper we will discuss the main motivation for QRNs, and give a brief overview of some methods of generating QRNs. Most importantly, we begin to consider whether Monte Carlo applications besides integration can be accelerated with QRNs. To this end we propose and study the applicability of QRNs for evaluating the largest and the smallest eigenvalue of a given matrix. This application makes use of QRNs in the evaluation of discrete Markov chains. In many areas of Monte Carlo transport, the evaluation of Markov chains is likewise undertaken. We believe that our success with this Markov chain example motivates further work in the application of QRNs to accelerate convergence of problems in transport Monte Carlo. The proposed quasi-Monte Carlo method based on Markov chain evaluation is a method for the computation of the extreme eigenvalues of a matrix. This method: can be considered as modication of the Power Method but it avoids the LU-decomposition and the forward and backward solving of the factored system at every iteration step in the Inverse Power Method and Inverse Shifted Power Method. The resolvent matrix is presented as a series. This representation permits us to use the well-known random walk process on the elements of the matrix to compute a matrix vector product with higher powers of the given matrix. This in turns leads to a substantial reduction in the computational eort required for solution. improves the convergence rate of the corresponding Monte Carlo method. Numerical tests are performed on sparse matrices of size 128, 1024, 2000 using PRNs and Sobol, Halton and Faure quasirandom sequences.
Automatic Detection of Abnormal EEG Signals Using WaveNet and LSTM

Neurological disorders have an extreme impact on global health, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is crucial for the diagnosis and management of these disorders. However, manual evaluation of EEG recordings is not only time-consuming but also requires specialized skills. This problem is exacerbated by the scarcity of trained neurologists in the healthcare sector, especially in low- and middle-income countries. These factors emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, there is a great interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of two distinct paths, WaveNet–Long Short-Term Memory (LSTM) and LSTM, for the automatic detection of abnormal raw EEG data. Through multiple ablation experiments, we demonstrated the effectiveness and importance of all parts of our proposed model. The performance of our proposed model was evaluated using TUH abnormal EEG Corpus V.2. 0.0. (TUAB) and achieved a high classification accuracy of 88.76%, which is higher than in the existing state-of-the-art research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUEP, without any hyperparameter tuning or adjustment. The obtained accuracy was 97.45% for the classification between normal and abnormal EEG recordings, confirming the robustness of our proposed model.
Redox potential as an important characteristic of the chemical and biological state of surface waters (review)

ABSTRACT This review summarises literature data on redox potential (Eh) as an essential characteristic of surface waters affecting their ecological and chemical state. We consider the influence of Eh on the migration of nutrients, metals, and metalloids in the ‘bottom sediments – water’ system and its role in the secondary pollution of water bodies. Hypoxia, anaerobic conditions, and reduced Eh leads to an increase in the concentration of nutrients, reduced forms of manganese and iron, due to their migration from bottom sediments to the aquatic environment. This, in turn, contributes to enhanced eutrophication of water bodies. We reviewed the data on the transformation of metal and metalloid compounds depends on the intensity of redox processes resulting their bioavailability, and toxicity to aquatic organisms. Humic substances, as the most common group of dissolved surface water organic substances, exhibit reducing properties and affect the coexisting metals and metalloid species. The intensity of redox processes in surface water will increase under climate change. The issue of redox transformation of elements will need to be considered in the context of developing measures to reduce surface water loading with primarily nutrients and metals, taking into account the possibility of internal pollution of water bodies.