Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning Neural Network-Based Adaptive Control for State and Input Time-Delayed Wheeled Mobile Robots

In this paper, a reinforcement learning-based adaptive control algorithm is proposed to solve the tracking problem of a discrete-time (DT) nonlinear state and input time delayed system of the wheeled mobile robot (WMR). With the typical model of the WMR transformed into an affine nonlinear DT system, a delay matrix function and appropriate Lyapunov–Krasovskii functionals are introduced to overcome the problems caused by the state and input time delays, respectively. Furthermore, with the approximation of the radial basis function neural networks (NNs), the adaptive controller, the critic NN, and action NN adaptive laws are defined to guarantee the uniform ultimate boundedness of all signals in the WMR system, and the tracking errors convergence to a small compact set to zero. Two examples of simulation are given to illustrate the effectiveness of the proposed algorithm.
Spatial regression with an informatively missing covariate: Application to mapping fine particulate matter

The United States Environmental Protection Agency has established a large network of stations to monitor fine particulate matter of <2.5 µm (PM2.5) that is known to be harmful to human health. Unfortunately, the network has limited spatial coverage, and stations often only measure PM2.5 every few days. Satellite‐measured aerosol optical depth (AOD) is a low‐cost surrogate with greater spatiotemporal coverage, and spatial regression models have established that including AOD as a covariate improves the spatial interpolation of PM2.5. However, AOD is often missing, and our analysis reveals that the conditions that lead to missing AOD are also conducive to high AOD. Therefore, naïve interpolation that ignores informative missingness may lead to bias. We propose a joint hierarchical model for PM2.5 and AOD that accounts for informatively missing AOD. We conduct a simulation study of the effects of ignoring informative missingness in the covariate and evaluate the performance of the proposed model. We apply the method to map daily PM2.5 in the Southeastern United States. Our analysis reveals statistically significant informative missingness and relationships between PM2.5 and AOD in many seasons after accounting for meteorological and land‐use variables.
On the Bounds for a Two-Dimensional Birth-Death Process with Catastrophes

The model of a two-dimensional birth-death process with possible catastrophes is studied. The upper bounds on the rate of convergence in some weighted norms and the corresponding perturbation bounds are obtained. In addition, we consider the detailed description of two examples with 1-periodic intensities and various types of death (service) rates. The bounds on the rate of convergence and the behavior of the corresponding mathematical expectations are obtained for each example.
Local pulse wave velocity estimated from small vibrations measured ultrasonically at multiple points on the arterial wall

Pulse wave velocity (PWV) is used as a diagnostic criterion for arteriosclerosis, a major cause of heart disease and cerebrovascular disease. However, there are several problems with conventional PWV measurement techniques. One is that a pulse wave is assumed to only have an incident component propagating at a constant speed from the heart to the femoral artery, and another is that PWV is only determined from a characteristic time such as the rise time of the blood pressure waveform. In this study, we noninvasively measured the velocity waveform of small vibrations at multiple points on the carotid arterial wall using ultrasound. Local PWV was determined by analyzing the phase component of the velocity waveform by the least squares method. This method allowed measurement of the time change of the PWV at approximately the arrival time of the pulse wave, which discriminates the period when the reflected component is not contaminated.
Identifying USP1 Inhibitors with Allosteric Effect on Its Triple Catalytic Center through Virtual Screening

Previous studies have shown that downregulation of ubiquitin-specific protease 1 (USP1) can inhibit cell proliferation and promote apoptosis, indicating that targeting USP1 with drugs is a potential treatment for cancer, especially breast and ovarian cancer. In this study, we performed virtual screening on a database containing about 1.37 million molecules using the pharmacophore model, multiple precision molecular docking algorithms, molecular mechanics/generalized born surface area (MM/GBSA), strain energy, and ADMET screening methods. We found that Z28551960, Z423064076, Z1037335610, and Z1129923146 compounds showed excellent performance in terms of binding energy with USP1, ADMET property evaluation, and binding dynamics with USP1, had high potential as USP1 inhibitors, and were identified as hits in virtual screening. In addition, we demonstrated for the first time through molecular dynamics simulations that those molecules can enhance the structural flexibility of the USP1 protein and reduce its structural compactness, providing a basis for protein conformational changes. In summary, our discovery of potential USP1 inhibitors with novel scaffolds provides a theoretical basis for further development of USP1 inhibitors.
A novel spectral-spatial classification technique for multispectral images using extended multi-attribute profiles and sparse autoencoder

ABSTRACT Image classification is a prominent topic and a challenging task in the field of remote sensing. Recently many various classification methods have been proposed for satellite images specifically the frameworks based on spectral-spatial feature extraction techniques. In this paper, a feature extraction strategy of multispectral data is taken into account in order to develop a new classification framework by combining Extended Multi-Attribute Profiles (EMAP) and Sparse Autoencoder (SAE). Extended Multi-Attribute Profiles is employed to extract the spatial information, then it is joined to the original spectral information to describe the spectral-spatial property of the multispectral images. The obtained features are fed into a Sparse Autoencoder as input. Finally, the learned spectral-spatial features are embedded into the Support Vector Machine (SVM) for classification. Experiments are conducted on two multispectral (MS) images such as we construct the ground truth maps of the corresponding images. Our approach based on EMAP and deep learning (DL), proves its huge potential to achieve a high classification accuracy in reasonable running time and outperforms traditional classifiers and others classification approaches.
Decision risk approach to the application of biological indicators in vapor phased hydrogen peroxide bio‐decontamination

The ensuring of sterile conditions during pharmaceutical production is of vital importance. Restricted access barrier systems may not be sterilized in the usual way. Instead, vaporous hydrogen peroxide decontamination is used. The success of the decontamination is checked by placing biological indicators (BIs), and upon their removal, putting them individually into broth for incubation. This technique does not allow counting the number of microorganisms but to detect the survival of any of them and thus counting the negative/positive answers. The Poisson distribution gives the connection between the number of viable spores and the number of positive (growth) BIs. Evaluation is based on the number of surviving BIs, which is probabilistic by nature. Acceptance limits are based on good engineering assumptions (“current industrial experience”), not on sound decision theory statistics (probability of error of the first and second kind). The reason of existence of surviving BI is either the process lethality failure or the limited penetrative capability of vaporous hydrogen peroxide. The latter phenomenon is called “rogue” BIs, and it should be distinguished. The method of the evaluation of the success of the decontamination is consistent with attribute sampling plans. In this paper, relations are explored to calculate the 2 types of error that can be applied in general during the qualification of a bio‐decontamination procedure. This is, to the best knowledge of the authors, the first ever application of attribute sampling plans to evaluate the results of a BI used bio‐decontamination process.
Millimetre molecular lines in Planck cold clumps

Abstract Galactic cold clumps have been identified from the Planck data (Planck Collaboration, 2011a, 2011b, 2015) as 10 342 cold (7 - 19 K) sources that stand out against a warmer environment, with the Early Cold Cores as a subsample of 915 most reliable detections. There is CO emission associated with the Planck Cold Clumps (PCCs), which has been observed with ground-based radio telescopes at higher resolution (Wu et al. 2012, Liu et al. 2014). A subset of PCCs have also been observed with Herschel at higher resolution (Juvela et al. 2012). A southern sub-sample of the PCCs has been observed with the Mopra 22-m telescope to study the molecular gas. The Mopra telescope has 3-mm, 7-mm and 12-mm bands, with broadband correlator configuration 8-GHz wide with 0.27-MHz channels, or multiple zoom bands 137-MHz wide with 33-KHz channels, within the 8 GHz. During the 2013 southern winter season we observed 10 clumps. This included observations in the 3-mm band of 12CO, 13CO and C18O and lines around 89 GHz (e.g. HCN, HCO+ and HNC), in the 7-mm band (e.g. CS) and in the 12-mm band (e.g. NH3). These observations were heterogenous, with sources selected by LST in gaps between observations of other projects, and band chosen by weather (i.e. in conditions unsuitable for higher frequencies, lower frequency bands were observed). During the 2014 season we observed 34 positions in 22 clumps, with zoom mode observations of lines around 89 GHz. This was a more well-defined sample of sources. The mapping of the CO lines shows good spatial correlation of the CO with the dust column density The CO isotoplogues show high optical depth in 12CO and 13CO. The lines of HCN, HCO+ and HNC are weak, but detected in many of the 2014 sample. We are modelling the line results to determine column densities, excitation temperatures and abundances, using tools such as radex (van der Tak et al. 2007).
The controllability of networks is determined by the density of low in-degree and low out-degree nodes

The problem of controllability of the dynamical state of a network is central in network theory and has wide applications ranging from network medicine to financial markets. The driver nodes of the network are the nodes that can bring the network to the desired dynamical state if an external signal is applied to them. Using the framework of structural controllability, here we show that the density of nodes with in-degree and out-degree equal to $1$ and $2$ determines the number of driver nodes in the network. Moreover we show that random networks with minimal in-degree and out-degree greater than 2, are always fully controllable by an infinitesimal fraction of driver nodes, regardless on the other properties of the degree distribution. Finally, based on these results, we propose an algorithm to improve the controllability of networks.
A Marketing Standpoint: What Marketers Can Teach Extension Professionals About Internet-Based Media

Extension audiences are turning to Internet-based media for information Extension professionals traditionally have provided them. In a qualitative study, we sought to relate the Internet-based media strategies of marketing professionals to the needs of Extension professionals to increase Extension professionals' success in using Internet-based media. Extension professionals can more effectively engage modern Extension audiences by implementing four strategies: knowing the research, knowing what makes audiences tick, making changes based on measurable data, and increasing the effectiveness of Internet-based media efforts.
Mock catalogues for large scale structure surveys and DESI

The upcoming Dark Energy Spectroscopic Instrument (DESI) and Euclid galaxy surveys aim to make the most precise galaxy clustering measurements yet in order to probe the nature of the mysterious dark energy that is thought to make up the majority of the energy density of the Universe today. To reach the required precision, it is essential that the systematics that affect these measurements are understood, which requires realistic mock galaxy catalogues. This thesis focuses on building a mock catalogue for the DESI Bright Galaxy Survey (BGS), and applications of this mock. We outline the methods used to create halo merger trees from N-body and Monte Carlo simulations, which is the first step towards creating a mock catalogue. We show how these methods can be extended beyond ΛCDM to warm dark matter, and show applications. We have developed a halo occupation distribution (HOD) method for creating a BGS mock catalogue from the Millennium-XXL (MXXL) simulation, with galaxies being assigned r-band magnitudes and g-r colours. The mock catalogue is able to reproduce the luminosity function and clustering of the Sloan Digital Sky Survey (SDSS) and Galaxy And Mass Assembly (GAMA) survey at different redshifts. The mock is used to quantify incompleteness in the DESI BGS due to fibre assignment, which depends on the surface density of galaxies, and to assess correlation function correction methods. An inverse pair weighting method is able to provide an unbiased correction on all scales. Finally, we show how the HOD methodology can be extended to construct mock catalogues for Euclid, and other large galaxy surveys.
An integrated in silico–in vitro approach for bioprinting core–shell bioarchitectures

Biological tissues possess a high degree of structural complexity characterized by curvature and stratification of different tissue layers. Despite recent advances in in vitro technology, current engineering solutions do not comprise both of these features. In this paper, we present an integrated in silico–in vitro strategy for the design and fabrication of biological barriers with controlled curvature and architecture. Analytical and computational tools combined with advanced bioprinting methods are employed to optimize living inks for bioprinting-structured core–shell constructs based on alginate. A finite element model is used to compute the hindered diffusion and crosslinking phenomena involved in the formation of core–shell structures and to predict the width of the shell as a function of material parameters. Constructs with a solid alginate-based shell and a solid, liquid, or air core can be reproducibly printed using the workflow. As a proof of concept, epithelial cells and fibroblasts were bioprinted respectively in a liquid core (10 mg/mL Pluronic) and in a solid shell (20 mg/mL alginate plus 20 mg/mL gelatin, used for providing the cells with adhesive moieties). These constructs had a roundness of 97.6% and an average diameter of 1500 ±136 μm. Moreover, their viability was close to monolayer controls (74.12% ± 22.07%) after a week in culture, and the paracellular transport was twice that of cell-free constructs, indicating cell polarization.
Collapsing of Non‐centred Parameterized MCMC Algorithms with Applications to Epidemic Models

Data augmentation is required for the implementation of many Markov chain Monte Carlo (MCMC) algorithms. The inclusion of augmented data can often lead to conditional distributions from well‐known probability distributions for some of the parameters in the model. In such cases, collapsing (integrating out parameters) has been shown to improve the performance of MCMC algorithms. We show how integrating out the infection rate parameter in epidemic models leads to efficient MCMC algorithms for two very different epidemic scenarios, final outcome data from a multitype SIR epidemic and longitudinal data from a spatial SI epidemic. The resulting MCMC algorithms give fresh insight into real‐life epidemic data sets.
High contribution of anthropogenic combustion sources to atmospheric inorganic reactive nitrogen in South China evidenced by isotopes

Abstract. Due to the intense release of reactive nitrogen (Nr) from anthropogenic activity, the source layout of atmospheric nitrogen aerosol has changed. To comprehensively clarify the level, sources, and environmental fate of NH4+ and NO3-, their concentrations and stable isotopes (δ15N) in fine particulate matter (PM2.5) were measured in a subtropical megacity of South China. The inorganic nitrogen (NH4+ and NO3-) was an essential part of atmospheric nitrogen aerosol, and the N-NH4+ and N-NO3- contributed 45.8 % and 23.2 % to total nitrogen (TN), respectively. The source contributions of NH4+ and NO3- were estimated by δ15N, suggesting that the dominant sources were from anthropogenic combustion activities, including coal combustion, biomass burning, and vehicles, contributing 63.2 % and 88.3 % to NH4+ and NO3-, respectively. In particular, biomass burning was the predominant source of NH4+ (27.9 %), whereas coal combustion was the dominant source of NO3- (40.4 %). This study emphasized the substantial impacts of human activities on inorganic Nr. With the rapid development of industry and transportation, nitrogen emissions will be even higher. The promotion of clean energy and efficient use of biomass would help to reduce nitrogen emissions and alleviate air pollution.
Utilizing e-learning and user loyalty with user satisfaction as mediating variable in public sector context

The advent of information technology has caused people to consider how they can make effective and efficient decisions in various activities. The implementation of information technology systems is expected to be advantageous in facilitating these activities because such systems can provide decision-making support and contribute to the success of endeavors in areas such as business, economic, social politics, and education. One common tool used in learning systems is e-learning applications. This research aims to analyze the effect of e-learning on user loyalty with user satisfaction. This research, conducted in Jakarta, is explanatory in nature, targeting individuals who have utilized e-learning applications in their activities, particularly in the field of public sector activities, with a sample size of 163 public sector employees. Data was collected through online questionnaires, and hypothesis testing was conducted through the PLS-SEM method. The results indicate that service quality and perceived value have positive impacts on user satisfaction, which in turn, positively influences user loyalty.
ESTIMATION OF MOISTURE DISTRIBUTION IN SAWN TIMBER USING COMPUTED TOMOGRAPHY

: Most of the physical, mechanical, and esthetic properties of wood products are affected by the drying of sawn timber. A better understanding of moisture transport in wood during kiln drying is necessary to obtain better quality products, shorter drying schedules, lower energy consumption, and a more sustainable process. Four-dimensional X-ray computed tomography (4DCT) (three-dimensional in space and one in time) with image processing techniques can be used to study the moisture content (MC) of sawn timber during kiln drying. The development of the technique is however made difficult by computational complexity and a lack of accurate experimental validation. In this study, a method relying on 4DCT has been developed using state-of-the-art image processing techniques. The method was validated by a regression analysis of the predicted MC against gravimetric measurements for different timber cross sections and different initial MC distributions on a significantly smaller scale than has previously been investigated. It is concluded that the MC can be estimated with an average uncertainty of ± 4.8 percentage points on a 10 mm scale. Sawmills could ultimately benefit from a better understanding of wood-water interactions and dry sawn timber more efficiently.
ROC-based Business Case Analysis for Predictive Maintenance – Applications in Aircraft Engine Monitoring

Predictive maintenance approaches leveraging integrated knowledge, fleet-wide data and machine-learning techniques allow for earlier warnings on impeding failures and for higher accuracy in remaining useful life predictions compared with traditional prognostics. However, in case relative to correctly predicted maintenance needs, missed detections or false alarms occur too often, follow-up costs, e.g. due to cascading effects or unnecessary inspection effort, can outweigh the advantages. Here, we show that a general cost-benefit analysis based on the Receiver Operating Characteristics (ROC) curve of a failure prediction algorithm allows deducing application-specific requirements on the failure prediction quality for achieving a net benefit. Moreover, various prediction algorithms can be compared and optimized regarding cost-efficiency. The value of the approach is demonstrated by an application example in aircraft engine maintenance showing that for reducing unscheduled engine removals by (more) accurate prediction of turbine blade failures, maximal cost-saving potentials of up to 16 Mio $ emerge per mature-run, widebody engine and per Mean-Time Between Removals (MTBRs). Here, realistic, literature-based assumptions on various costs, failure probability and algorithm performance were incorporated and varied within sensible limits. As a further key result, the value of data for predictive maintenance purposes is impressively demonstrated. Compared with the net benefit achievable by failure prediction of a pure physics-based damage accumulation model and by turbine operating data, an up to 6 Mio. $ higher cost-saving potential per MTBRs was shown to emerge from a literature-based hybrid approach fusing physics and further sources of data, e.g. on manufacturing, geography and environment as well as customer and inspection information by means of machine learning techniques. Generalized applications of the presented cost-benefit analysis approach e.g. to optimize costs associated with engine workscope planning or other system maintenance are discussed.
Efficient Compressed Sensing Based MRI Reconstruction using Nonconvex Total Variation Penalties

This work addresses the problem of Magnetic Resonance Image Reconstruction from highly sub-sampled measurements in the Fourier domain. It is modeled as a constrained minimization problem, where the objective function is a non-convex function of the gradient of the unknown image and the constraints are given by the data fidelity term. We propose an algorithm, Fast Non Convex Reweighted (FNCR), where the constrained problem is solved by a reweighting scheme, as a strategy to overcome the non-convexity of the objective function, with an adaptive adjustment of the penalization parameter. We propose a fast iterative algorithm and we can prove that it converges to a local minimum because the constrained problem satisfies the Kurdyka-Lojasiewicz property. Moreover the adaptation of non convex l0 approximation and penalization parameters, by means of a continuation technique, allows us to obtain good quality solutions, avoiding to get stuck in unwanted local minima. Some numerical experiments performed on MRI sub-sampled data show the efficiency of the algorithm and the accuracy of the solution.
The Use of Machine Learning in Supply Chain Management, A Systematic Review

This paper aims to analyze and review the most recent scientific literature regarding the implementation of Machine Learning (ML) in Supply Chain Management (SCM). Moreover, an examination was conducted to find out what areas and industries were addressed in the use cases, and how ML may impact SCM. A Systematic Literature Review (SLR) was conducted for this purpose. It was found that the applied examples mainly used ML's prediction capabilities, and some industries such as health care and logistics were the main focus areas. Then, discussions were developed based on the results of the systematic literature analysis, which can be used as motivation for future research.