Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis on Damage Measure of Vertex Importance in Complex Networks

To research the vertex importance index—Damage in complex networks from the perspective of robustness of complex networks,the Damage measures of vertices in synthetic networks including Barabasi-Albert,Erdos-Renyi,tree-like networks and real complex networks are theoretically analyzed.The influences of Damage on network functions and the correlations between Damage and degrees are empirically analyzed.The resisting abilities of real networks against Damage attacks and degree attacks are compared by statistical analyses.The theoretical and empirical analyses show that:there exist many non-ignorable vertices with large Damage in complex networks;Damage is one of the non-replaceable indexes measuring the importance of vertices;vertices with large Damage value are important for maintaining the functionality of networks.For a variety of networks,the results from comparison of the degree attack with the Damage attack processes show the Damage attack before a crossover point is more destructive than the degree attack.
TEMPERATURE DEPENDENCE OF THE AMORPHOUS SmTbFeCo MAGNETO-OPTICAL FILMS

A mean- field theory is developed to research the temperature dependence of the amorphous SmTbFeCo thin films. It is shown that with available Sm substitution of Tb, the composition temperature Tcomp decreases, Curie temperatureTC remains unchanged, Kerr angle becomes larger, the saturation magnetization Ms at room temperature increases. Therefore, LRE-HRE-TM amorphous films can be used as the high density magneto-optical media in a wide range of temperature. However, the Sm substitution of TM does not improve the magneto-optical performance.
Electrodeposited Carbonyl Functional Polymers as Suitable Supports for Preparation of the First-Generation Biosensors

The aim of this electrochemical study was to ascertain which type of electrochemically deposited carbonyl functionalized polymer represents the most suitable electrode substrate for direct covalent immobilization of biological catalysts (enzymes). For this purpose, a triad of amperometric biosensors differing in the type of conductive polymers (poly-vanillin, poly-trans-cinnamaldehyde, and poly-4-hydroxybenzaldehyde) and in the functioning of selected enzymes (tyrosinase and alkaline phosphatase) has been compared for the biosensing of neurotransmitters (dopamine, epinephrine, norepinephrine, and serotonin) and phenyl phosphates (p-aminophenyl phosphate and hydroquinone diphosphate). The individual layers of the polymers were electrochemically deposited onto commercially available screen-printed carbon electrodes (type C110) using repetitive potential cycling in the linear voltammetric mode. Their characterization was subsequently performed by SEM imaging and attenuated total reflectance FTIR spectroscopy. Molecules of enzymes were covalently bonded to the free carbonyl groups in polymers via the Schiff base formation, in some cases even with the use of special cross-linkers. The as-prepared biosensors have been examined using cyclic voltammetry and amperometric detection. In this way, the role of the carbonyl groups embedded in the polymeric structure was defined with respect to the efficiency of binding enzymes, and consequently, via the final (electro)analytical performance.
One-Pot Synthesis of Silica-Coated Gold Nanostructures Loaded with Cyanine 5.5 for Cell Imaging by SERS Spectroscopy

Anisotropic gold nanoparticles have been recognized as promising agents for medical diagnostics and cancer therapy due to their wide functionality, photothermal effect, and ability for optical signal amplification in the near-infrared range. In this work, a simple and rapid method for the preparation of bone-shaped gold nanoparticles coated with a dye-impregnated silica shell with an aminated surface is proposed. The possibility of further functionalization the nanostructures with a delivery vector using folic acid as an example is demonstrated. The average size of the resulting tags does not exceed 70 nm, meeting the criteria of cell endocytosis. The prepared tags exhibit surface-enhanced Raman scattering (SERS) spectra at excitation with lasers of 632.8 and 785 nm. Cell imaging is performed on HeLa cells based on the most pronounced SERS bands as a tracking signal. The obtained images, along with scanning electron microscopy of cell samples, revealed the tendency of tags to agglomerate during endocytosis followed by the “hot spots” effect. To evaluate the toxic and proliferative effect of the nanotags, an MTT assay was performed with two HeLa and HEP G2 cell lines. The results revealed higher viability for HEP G2 cells.
Min-sum decoding algorithm for LDPC codes based on integer operation

The LDPC codes decoding algorithm over additive white gaussian noise(AWGN) channels was studied,and a min-sum algorithm based on integer operation was proposed for decoding LDPC codes.All the variables were fixed length integers in this algorithm,which was very convenient for hardware implementation.At the signal-to-noise ratios(SNR) above 3dB,its performance was equal to that of sum-product algorithm whose variables were high precision floating point numbers.Also,a coefficient was designed to modify the min-sum algorithm,whose decoding performance was improved greatly especially at low SNR.And another achievement had been found that this algorithm had a lower error floor at high SNR.
Modeling of Organic Waste Classification as Raw Materials for Briquettes using Machine Learning Approach

—The existence of organic waste must be utilized by the community so that it does not only end up in landfills but can also be processed into something constructive so that it is useful and has high economic value. Organic waste can be converted into raw materials to manufacture of biomass briquettes. Machine learning techniques were developed for technological applications, object detection, and categorization. Methods with artificial reasoning networks that use a number of algorithms, such as the Naive Bayes Classifier, will work together in determining and identifying certain characteristics in a digital data set. The manufacturing method goes through several processes with a waste classification model as a source of learning data. The image data is based on five types: coconut shells, sawdust, corn cobs, rice husks, and plant leaves. The research aims to identify and classify types of waste both organically and non-organically so that it will make it easier to sort waste. The results of testing the organic waste application from digital images have an accuracy rate of 97%. The model design carried out in training data is useful for producing a data model.
MODEL-BASED INTELLIGENT SIMULATION OF REFRIGERATION SYSTEM

In order to improve the adaptability of simulation method to diversity and complexity of refrigeration and air-conditioning appliances, intelligent simulation theory for refrigeration system should be set up, which is based on mathematical model combining with artificial intelligence. This paper introduces the achievements of Shanghai Jiaotong University in this field and points out the further investigations that should be done in the future. Researches in this field are not only of benefit to the modernization of design method for refrig- eration and air-conditioning system, but also to other thermal systems.
Relative Orientation Based on Multiple Conjugate Features

To resolve the current problems of relative orientation caused by lacking of obvious feature points in applications of industrial and architectural photogrammetry,a new method of relative orientation based on multiple conjugate features is proposed based on the theory of generalized point photogrammetry.The models of indepen-dent and dependent relative orientation using conjugate lines and circular curves are addressed respectively.Experimental results show that the proposed approach is feasible in practice.It can get reliable results even in the case of relative orientation with conjugate lines,circular curves and a few points.
Ultrafast <italic>I<sub>D</sub></italic> <italic>V<sub>G</sub></italic> Technique for Reliable Cryogenic Device Characterization

An in-depth understanding of the transient operation of devices at cryogenic temperatures remains experimentally elusive. However, the impact of these transients has recently become important in efforts to develop both electronics to support quantum information science as well as cryogenic highperformance computing. In this paper, we discuss a fast time-dependent device characterization technique, capable of examining the charge trapping dynamics of devices operating at cryogenic temperatures. Careful calibrations allow for the acquisition of accurate fast I-V and transconductance transients down to 20 ns for devices operating down to 8 K. The trap charging dynamics was monitored via shifts in both threshold voltage and transconductance. The combination of fast measurements and cold temperatures were used to shift the observable measurement window to reveal charge trapping/de-trapping time dynamics of both fast and slow traps in high-k devices to demonstrate the utility of the fast I-V for cryogenic device
Accuracy in the Marginal Adaptation and/or Internal Adaptation of Full-coverage Fixed Prostheses Made with Digital Impressions and Conventional Impressions: A Systematic Review

The study aimed to verify compliance of the systematic reviews with the requirements established by the scientific community and demonstrate the validity and reliability of the systematic reviews conducted on the accuracy (marginal adaptation and/or internal adaptation) of the full-coverage fixed prostheses made with digital impressions versus conventional impressions. A search was performed for systematic reviews in three electronic databases, PubMed, Scopus, and Web of Science, as well as in the gray literature. In the search strategy, medical subject heading (MeSH) words were used in PubMed, and free terms were used for the titles and abstracts of each article. Each keyword was separated by the Boolean operator OR and later combined with the Boolean operator AND. Six systematic reviews were included for qualitative synthesis. To assess the methodological quality of the included systematic reviews, the AMSTAR 2 tool was used. The search yielded 131 studies, of which 78 remained after removing duplicates. The title and abstract of each chosen study were assessed, and 22 articles were included for full-text reading. Finally, six studies were included, of which three studies were considered to have low confidence, while the other three were considered to have critically low confidence. In addition, the six SRs evaluated the adaptation or marginal fit, while only three studies measured internal adaptation. The use of digital impressions in single fixed prostheses maintains a marginal level within the limit of clinical acceptability; however, the methodological quality of systematic reviews is poor, according to the AMSTAR 2 tool.
Robust Nonlinear Filtering: A Non-Bayesian Approach

Today, the field of practical estimation is dominated by linear-Bayesian estimators as these familiar techniques are computationally efficient and can be quickly tailored to many systems of interest. However, beyond the common weaknesses of linear estimators to non-Gaussian noises and nonlinear transformations, Bayes' rule itself lacks robustness, as it inherently assumes that all statistical models are exactly known. Therefore, in lieu of filters outfitted with linear-Bayesian updates, this work proposes a method to derive nonlinear, non-Bayesian updates from first principles using the theory of generalized variational inference (GVI)—an optimization approach to information fusion. By selecting different divergence measures, loss functions, and feasible distributions, a wide variety of robust/conservative filter updates are quickly built and tested using numerical optimization. From these GVI candidates, a suitable update that uses the robust <inline-formula><tex-math notation="LaTeX">$\gamma$</tex-math></inline-formula>-loss function is selected based upon its desirable estimation behavior. Via analytical optimization and Gaussian mixture modeling, the <inline-formula><tex-math notation="LaTeX">$\gamma$</tex-math></inline-formula>-loss GVI update is realized as a nonlinear, closed-form filter that is well-suited for practical estimation.
Precise event sampling‐based data locality tools for AMD multicore architectures

We propose ComDetective +$$ {}^{+} $$ , an inter‐thread communication analyzer, and ReuseTracker +$$ {}^{+} $$ , a reuse distance analyzer, that leverage the hardware features in AMD processors to support low‐overhead profiling. Both tools employ the instruction‐based sampling (IBS) facility and debug registers in AMD processors to detect inter‐thread communication and data reuse. Different from prior arts, ComDetective +$$ {}^{+} $$ differentiates the communication into true and false sharing, and ReuseTracker +$$ {}^{+} $$ measures reuse distance in private and shared caches by also considering cache line invalidation with low overhead. Both tools can attribute the communications and reuses to source code lines. To our knowledge these tools are two of the few profiling tools designed specifically for AMD x86 architectures using IBS. Our tools are timely and relevant considering the rise in numbers of AMD processor based data centers and HPC systems. We perform experiments to evaluate the accuracy and overheads of the proposed tools on an AMD machine with two‐socket EPYC 7352 processors. ComDetective +$$ {}^{+} $$ exhibits high accuracy while introducing 5.14 ×$$ \times $$ runtime and 1.4 ×$$ \times $$ memory overheads. ReuseTracker +$$ {}^{+} $$ also displays high accuracy, which is 95%, with 11.76 ×$$ \times $$ runtime and 1.46 ×$$ \times $$ memory overheads. These overheads are much lower than the overheads of existing simulators and code instrumentation‐based tools. Lastly, we demonstrate the usage of the tools by having ComDetective +$$ {}^{+} $$ and ReuseTracker +$$ {}^{+} $$ facilitate the code refactoring of two data mining benchmarks to improve their performance by up to 29%.
Functions with Unusual Differentiability Properties

The monotone increasing Cantor function is used as a "machine" for building examples of strange functions with interesting properties both from the Fractal Measure Theory and from Singular Functions viewpoints. We give examples showing functions f where: (i) f is increasing with zero derivative a.e. (i.e. singular) and it has associated a Lebesgue-Stieltjes measure df of prescribed Hausdorff dimension � 2 (0,1), (ii) f is of monotonic type on no interval (MTNI) and singular; (iii) f is a continuous nowhere differentiable function, and (iv) f is absolutely continuous and monotone on no interval (MNI). Mathematics Subject Classification 2010: 26A27, 26A30, 26A45, 26A46, 26A48.
Automatic prostate and prostate zones segmentation of magnetic resonance images using convolutional neural networks

Magnetic resonance imaging (MRI) provides detailed anatomical images of the prostate (PR) and its zones. The importance of segmenting the prostate and the prostate zones, such as the central zone (CZ) and the peripheral zone (PZ) lies in the fact that the diagnostic guidelines differ depending on in which zone the lesion is located. Thus, automatic prostate and prostate zone segmentation from MR images is an important topic for many diagnostic and therapeutic purposes. However, the prostate tissue heterogeneity and the huge varieties of prostate shapes among patients make this task very challenging. Therefore, we propose a new neural network named Dense U-net inspired by the state-of-the-art DenseNet and U-net to automatically segment prostate and prostate zones. It was trained on 141 patient datasets and tested on 47 patient datasets with axial T2-weighted images in four-fold cross-validation manner. The network can successfully segment the gland and its subsequent zones. This Dense U-net compared with the state-of-the-art U-net achieved an average dice score for the whole prostate of 91.2± 0.8% vs. 89.2 ± 0.8%, for CZ of 89.2± 0.2% vs. 87.4 ± 0.2%, and for PZ of 76.4± 0.2% vs. 74.0± 0.2%. The experimental results show that the developed Dense U-net was more accurate than the state-of-the-art U-net for prostate and prostate zone segmentation.
Morphological Edge Detection Technology in Digital Image Processing

The aim of edge detection is usually to use the image that is more similar than sketch picture to express the feature and outline of object. In actual image, edge consists of saltation gray scale pixels . It plays on important role in digital image processing and analysis. This paper synthesizes newest literatures, analyses many kinds of edge detection techniques based on mathematical morphology: multi-scale morphologic edge detection , multi-grade mean morphologic edge detection, morphological edge detector based on partial differential equations , combined edge detecting algorithm based on equalization and morphology , edge detector using multi-structure element based on coordinate (logic,)and so on. The paper also compares their pluses and minuses synthetically , and probes into its development of edge detection.
Iterative learning control of variable gain with initial state study

For a class of nonlinear systems,a new learning control algorithm is proposed,which increases iterative learning rule of initial state of system on the base of iterative learning control with variable learning gain.By using the operator theory,it is proved that the output of system can track the expected trajectory completely after the iterative learning of system with initial state disturbance,and the convergent condition for the spectral radius form of the algorithm is given.Compared with the tranditional iterative learning control,the proposed algorithm not only significantly improves the convergent speed,but also solves the initial state disturbance problem of the iterative learning control with variable learning gain.Simulation results show the effectiveness of the proposed algorithm.
Potential of Indigofera tinctoria Natural Dyes Compost on Maize Vegetative Growth

Expanding sustainable agriculture approaches is critical to increase crop growth and productivity. This farming system should involve organic fertilization and not neglect the recycle of organic waste. One of the organic wastes that can be used as compost is Indigofera tinctoria natural dye waste. The research objective was to examine the vegetative growth response of corn plants to Indigofera tinctoria compost. The design used was a complete randomized block design with one factor of fertilization doses with 3 six levels namely 2.91; 5.83; 8.75; 11.67; 14.58 tons/ha, and chemical fertilizers as controls. Indigofera tinctoria compost meets the standards as organic fertilizer according to the Ministry of Agriculture with characteristics of 18.2% water content, 52.48% organic C, 90.48% organic matter, 2.84% total nitrogen, 1.54% total phosphate, total potassium 2.44%, 1.30% available nitrogen, 0.92% available phosphate, 1.52% available potassium and 60 cmol/kg cation exchange capacity. The dose of compost significantly affected on plant height of 2-4 WAP, stem diameter of 2 WAP , and root biomass of 2 WAP. The compost dose of 5.83 tons/ha has increased root growth and plant height.
Visualizing big data via a mixture of PARAMAP and Isomap

Dimension reduction strives to represent higher dimensional data by a lower-dimensional structure. A famous approach by Carroll called Parametric Mapping or PARAMAP (Shepard & Carroll, 1966) works by iterative minimization of a loss function measuring the smoothness or continuity of the mapping from the lower dimensional representation to the original data. The algorithm was revitalized with essential modifications (Akkucuk & Carroll, 2006). Even though the algorithm was modified, it still needed to make a large number of randomly generated starts. In this paper we discuss the use of a variant of the Isomap method (Tenenbaum et al., 2000) to obtain a starting framework to replace the random starts. The core set of landmark points are selected by a special procedure akin to selection of seeds for the k-means algorithm. These core set of landmark points are used to create a rational start for running the PARAMAP algorithm only once but effectively reach a global minimum. Since Isomap is faster and less inclined to local optimum problems than PARAMAP, and the iterative process involved in adding new points to the configuration will be less time consuming (since only one starting configuration is used), we believe the resulting method should be better suited to deal with large data sets, and more prone to obtain an acceptable solution in realistic time.
БИФУРКАЦИЯ ЭНЕРГИИ СВЯЗИ И ХАОС В АТОМНЫХ ЯДРАХ BINDING ENERGY BIFURCATION AND CHAOS IN ATOMIC NUCLEI Трунев Александр Петрович к.ф.-м.н., Ph.D.

Director, A&E Trounev IT Consulting, Toronto, Canada В работе рассмотрена модель хаотического поведения нуклонов в атомных ядрах , построенная на основе модели ядерных взаимодействий и статистики Ферми -Дирака The model of chaotic behavior of nucleons in nuclei, based on the model of nuclear interactions and the Fermi-Dirac statistics is discussed Ключевые слова : НЕЙТРОН , ПРОТОН , ФЕРМИ -ДИРАКА СТАТИСТИКА , ХАОС , ЭНЕРГИЯ СВЯЗИ , ЯДРО Keywords: BINDING ENERGY, CHAOS, FERMI-DIRAC STATISTICS, PROTON, NEUTRON, NUCLEI
An approach for hyperspectral image classification utilization spatial-spectral combined kernel SVM

Hyperspectral images belong to high-dimensional data having a lot of redundancy information when they are directly used to classification. Support vector machine (SVM) can be employed to map hyperspectral data to high dimensional space effectively and make them linearly separable. In this paper, spectral and spatial information of hyperspectral images were used to construct SVM kernel function respectively. This paper proposed a hyperspectral image classification method utilization spatial-spectral combined kernel SVM in order to improve classification accuracy. The proposed method was used to classify AVIRIS hyperspectral images. The results demonstrated that the proposed SVM method can achieve 96.13% overall accuracy for the single category classification and 84.81% overall accuracy for multi-class classification only using ten percent of the total samples as the training samples. That is to say, the proposed method can make full use of the spectral information and spatial information of hyperspectral data, and effectively distinguish different categories compared with the traditional SVM for classification.
Calibrating Drift of Short-Rate Models with Jump-Diffusion

The drift or the mean-reversion level of short-rate models under jump-diffusion is derived to fit the initial term-structure of zero-coupon bond. In particular, the drift is obtained for Hull-White and Cox-Ingersoll-Ross short-rate models. The purpose of obtaining the drift is for the Monte-Carlo simulations for pricing exotic equity options under stochastic interest rates and exotic interest rate derivatives. The theoretical expressions for the drift are verified by Monte-Carlo simulations of the short-rate and bond paths, and show that the expectation of the discounted entities reproduce the initial zero-coupon bond term-structure. For the Cox-Ingersoll-Ross model the drift is obtained by a numerical computation of a second-order ordinary differential equation using a perturbation method. The correctness of this method is verified by the Monte-Carlo results. Further, the bond partial differential equation (PDE) for the Cox-Ingersoll-Ross jump-diffusion short-rate model is also solved numerically and the results reproduce the initial zero-coupon bond term-structure. This bond PDE computation verifies the self-consistency of the drift computation, and also validates the numerical boundary conditions used in the finite-difference computations of this final-value problem. Similar results are presented from the finite-difference computation of the Hull-White PDE also.