Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Watershed Algorithm to Cell Image Segmentation

To overcome the difficulty caused by the overlapping of cells in the image analysis for cancer diagnosis, a new cell image analysis method is proposed based on watershed algorithm to separate the clustering cells into single ones. Firstly, a distance map is produced to determine the number of seeds from the clustering cells based on iterative erosion. Then, the ultra erosion aggregates are taken as the seeds to separate the cells. Finally, the images are grown from the seed regions until all of the image pixels except the boundaries of convex have been assimilated. The design and implementation of the algorithm and experimental results are presented.
ICCM2015(Paper ID:1148): DPD Simulation of the Movement and Deformation of Bioconcave Cells

This paper presents a dissipative particle dynamics (DPDs) method for investigating the movement and deformation of biconcave shape red blood cells (RBCs) with the worm-like chain (WLC) bead spring. First, the stretching of a RBC is modeled and the obtained shape evolution of the cell agrees well with experimental results. Second, the movement and deformation of a RBC in shear flows are investigated and three typical modes (tumbling, intermittent and tank-treading) are observed. Lastly, an illustrating example of multi-RBCs in Poiseuille flow in a tube is simulated. We conclude that the presented DPD method with WLC spring can effectively model the movement and deformation of bioconcave cells.
Small‐scale spatial variability of Technosol properties in a chronosequence of reclamation of dredged‐sediment landfills

Active reclamation is often necessary to ensure a transformation of mining waste into Technosols—“soils dominated or strongly influenced by human‐made material”—and restore its utility and environmental value. The objective of this study is to assess the spatial variation of the physicochemical properties relevant for reclamation and Technosol formation on dredged‐sediment landfills left by alluvial gold mining in a chronosequence of reclamation (0, 4, 8, and 12 years). We hypothesize a higher spatial dependency of most soil properties with increasing time of Technosol formation and an overall homogenization of the soil resulting from pedogenetic processes. Our results show early signs of Technosol pedogenesis with strong physicochemical changes after only few years of formation. We observed that older Technosols (>4 years) are more acidic, have less nutrient and organic matter content, higher exchangeable cations, and less signs of compaction than nonrevegetated landfills. The content of organic matter, phosphorus, and exchangeable cations show the highest spatial variability in Technosols of all ages. In older Technosols, most soil properties showed less spatial variability than in younger Technosols. A multivariate geostatistical approach allowed the delineation of zones with distinctive physicochemical properties within areas of the chronosequence. The results show that reclamation and Technosol formation lead to spatially dependent fragmentation processes reflected in more and smaller clustered zones in Technosols after 12 years of formation. From the perspective of reclamation management, understanding the spatial variability of highly heterogeneous Technosols where substantial changes can be observed within small distances can support the development of site‐specific reclamation strategies suitable to the characteristics of each field as well as the determination of its potential uses.
Teaching mathematics for social justice beliefs scale: psychometrics and practices in teacher education

This paper reports on the development of the Teaching Mathematics for Social Justice beliefs (TMfSJ-B) scale and its psychometric properties. Preservice elementary education teachers enrolled in teacher education programmes at seven universities completed multiple surveys designed to understand the evolution of attitudes and beliefs about teaching mathematics. To our knowledge, the TMfSJ-B scale is the only survey specifically designed to measure pre- and in-service teachers’ beliefs about teaching mathematics for social justice. Confirmatory factor analyses (n = 655) provided here indicate that the TMfSJ-B scale is a valid and reliable assessment of beliefs about social justice in mathematics teacher education, with three factors: (1) value of mathematics for all students; (2) integration of social justice into mathematics instruction; and (3) mathematics and its relation to inclusiveness. The purpose of this study was to validate measures produced by the TMfSJ-beliefs scale and discuss its potential use in teacher education programmes and research.
Geometric mean labeling of graphs

In this paper we introduce a new labeling called Geometric mean labeling. A graph G (V, E) with p vertices and q edges is said to be a Geometric mean graph if it is possible to label the vertices XEV with distinct labels f(x) from 1,2 …. q+1 in such a way that when each edge e = uv is labeled with (or) then the edge labels are distinct.
Spectral and Pseudospectral Methods for Eigenvalue and Nonseparable Boundary Value Problems

Abstract It is shown that spectral and pseudospectral methods can lead to great savings in numerical efficiency-typically, at least a factor of four in storage and eight in operation count-in comparison with finite-difference methods for solving eigenvalue and nonseparable, noniterative boundary value problems at the cost of only a a slight amount of additional programming. The special problems or apparent problems that geophysical boundary layers, critical latitudes and critical levels and real data create for spectral algorithms are also discussed along with when and how these can be solved.
A Review of Milgram and Kishino’s Reality-Virtuality Continuum and a Mathematical Formalization for Combining Multiple Reality-Virtuality Continua

—We explore in this paper theoretical contributions that are related to Milgram and Kishino’s Reality Virtuality Continuum by conducting a systematic literature review. From this study, we draw inspiration for our proposed mathematical formalization of combining multiple Reality-Virtuality Continua in a single, mixed reality experience. Also, we provide a definition for XR transition protocol . To complete our contribution, we discuss two potential examples that will exemplify our formalization and identify future work to be addressed.
SAR IMAGE SEGMENTATION BASED ON WATERSHED AND SPECTRAL CLUSTERING

Spectral clustering is a similarity-based clustering algorithm on graph theory.When the processed image is very huge,it is very difficult and time-consuming to compute affinity matrix and its eigenvalues and eigenvectors.Aiming at the characteristics of synthetic aperture radar(SAR) images,a two-stage image segmentation algorithm was proposed,in which watershed was used to produce over-segmentation and an improved spectral clustering algorithm was applied to perform final clustering.The new algorithm can not only reduce the noise in SAR images and keep their boundary very well but also is valuable to the application of higher demand for time.To verify the performance of the proposed algorithm,it was applied to segment SAR images,and better segmentation results were obtained.
Models of similarity in complex networks

The analysis of networks describing many social, economic, technological, biological and other systems has attracted a lot of attention last decades. Since most of these complex systems evolve over time, there is a need to investigate the changes, which appear in the system, in order to assess the sustainability of the network and to identify stable periods. In the literature, there have been developed a large number of models that measure the similarity among the networks. There also exist some surveys, which consider a limited number of similarity measures and then perform their correlation analysis, discuss their properties or assess their performances on synthetic benchmarks or real networks. The aim of the article is to extend these studies. The article considers 39 graph distance measures and compares them on simple graphs, random graph models and real networks. The author also evaluates the performance of the models in order to identify which of them can be applied to large networks. The results of the study reveal some important aspects of existing similarity models and provide a better understanding of their advantages and disadvantages. The major finding of the work is that many graph similarity measures of different nature are well correlated and that some comprehensive methods are well agreed with simple models. Such information can be used for the choice of appropriate similarity measure as well as for further development of new models for similarity assessment in network structures.
Finite element analysis of stress and strain of litchi under different clamping methods

Litchi fruit is rich in water and nutritional value. Machine picking can greatly reduce production costs. But if mechanical damage occurs during picking, it will seriously affect subsequent storage and transportation. In order to reduce the mechanical damage caused by excessive clamping force of the end effector on litchi, this study used the finite element model to analyze the mechanical damage caused by different clamping methods on the pericarp, sarcocarp and putamen of litchi under the basic clamping conditions. The results showed that the effect of clamping the fruit below the waist by double finger lifting was excellent. The maximum deformation of litchi was 42. 06μm, the average equivalent elastic strain was 3.90 × 10-4 and the maximum equivalent stress was 62857 Pa. This method effectively reduced the maximum stress and average strain in the process of grasping and reduced the mechanical damage caused by the end effector to the pericarp and sarcocarp. At the same time, it can provide some reference for the similar characteristics of fresh fruit picking, sorting, transportation and packaging.
High-G MEMS Accelerometer Calibration Denoising Method Based on EMD and Time-Frequency Peak Filtering

In order to remove noise generated during the accelerometer calibration process, an accelerometer denoising method based on empirical mode decomposition (EMD) and time-frequency peak filtering (TFPF) is proposed in this paper. Firstly, a new design of the accelerometer structure is introduced and analyzed by finite element analysis software. Then, an algorithm combining EMD and TFPF is proposed for the first time to deal with the noise of the accelerometer calibration process. Specific steps taken are to remove the intrinsic mode function (IMF) component of the high frequency band after the EMD decomposition, and then to use the TFPF algorithm to process the IMF component of the medium frequency band; meanwhile, the IMF component of the low frequency band is reserved, and finally the signal is reconstructed. The reconstruction results show that the algorithm can effectively suppress the random noise generated during the calibration process. The results of spectrum analysis show that EMD + TFPF can effectively protect the characteristics of the original signal and that the error can be controlled within 0.5%. Finally, Allan variance is used to analyze the results of the three methods to verify the filtering effect. The results show that the filtering effect of EMD + TFPF is the most obvious, being 97.4% higher than the original data.
A new feasible interior point method to nonnegative linear least squares problems

A new method of solving nonnegative linear least squares problems was presented.Firstly,nonnegative linear least squares problem was transformed into linear complementarity problem.By combining Newton direction and centering direction,the search direction by solving a linear system was obtained.Then a feasible interior point algorithm for nonnegative linear least squares problem was established and the results showed that this method was polynomial in complexity.At last,some numerical examples to indicate that the method is feasible and effective were given.
Nodes Neighborhood Relation-based Construction Algorithm for Minimum Connected Domination Set

Connected dominating set is very important in Wireless Sensor Networks(WSN) as a virtual backbone for communication and routing between nodes.This paper proposes a nodes’ neighborhood based algorithm to construct a Minimum Connected Dominating Set(MCDS) in WSN.The complexities of time and message are O(nlogn) and O(n),respectively.Regarding to the topological changes due to power constraint,it presents a repair algorithm to reconstruct the MCDS.Theoretical analysis and simulations both demonstrate the correctness and performance of the algorithm.
Scattering of a transverse elastic wave by an elastic sphere in a solid medium

Cross sections are computed for the scattering of a plane transverse elastic wave by an elastic sphere in an infinite isotropic homogeneous elastic solid. Analytic expressions are derived for the matrix elements indicated by Einspruch, Witterholt, and Truell, and the resulting matrix equations are solved numerically. The dependence of the scattering cross section upon K1a (K1 is the transverse propagation constant, a is the obstacle radius) over the range 0.01–10 is computed for various combinations of host and scatterer materials. The sensitivity of the cross section and its component terms to the elastic properties of the host and scatterer materials, and their behavior in the Rayleigh limit approximation are discussed. The calculations include the case of a constant host with a varying obstacle, and a constant scatterer in a varying host medium. It is found that most of the examples tested can be grouped conveniently into four classes, with a fifth category containing unstable results; this classificat.
Low Pass Filter and Band Pass Filter Design

This chapter designs low pass filters (LPF) and band pass filters (BPF). It uses LPF or BPF to extract desired spectral components and explains the frequency characteristics and impulse responses of LPF and BPF. The chapter provides step-by-step code exercises and instructions to implement execution sequences. The MATLAB code fragment generates a vector Hw_vector, the sampled version of H( ? > ). Each element of Hw_vector will be calculated through a separate numerical integration of ht, the sampled version of h(t). The magnitude spectrum |H( ? > )| and phase spectrum H( ? > ) are also plotted. In a time invariant linear system like the LPFs the authors have designed, the input signal undergoes the same amount of delay as the delay of h(t). The chapter is designed to help teach and understand communication systems using a classroom-tested, active learning approach.
DaGzang: a synthetic data generator for cross-domain recommendation services

Research on cross-domain recommendation systems (CDRS) has shown efficiency by leveraging the overlapping associations between domains in order to generate more encompassing user models and better recommendations. Nonetheless, if there is no dataset belonging to a specific domain, it is a challenge to generate recommendations in CDRS. In addition, finding these overlapping associations in the real world is generally tricky, and it makes its application to actual services hard. Considering these issues, this study aims to present a synthetic data generation platform (called DaGzang) for cross-domain recommendation systems. The DaGzang platform works according to the complete loop, and it consists of the following three steps: (i) detecting the overlap association (data distribution pattern) between the real-world datasets, (ii) generating synthetic datasets based on these overlap associations, and (iii) evaluating the quality of the generated synthetic datasets. The real-world datasets in our experiments were collected from Amazon’s e-commercial website. To validate the usefulness of the synthetic datasets generated from DaGzang, we embed these datasets into our cross-domain recommender system, called DakGalBi. We then evaluate the recommendations generated from DakGalBi with collaborative filtering (CF) algorithms, user-based CF, and item-based CF. Mean absolute error (MAE) and root mean square error (RMSE) metrics are measured to evaluate the performance of collaborative filtering (CF) CDRS. In particular, the highest performance of the three recommendation methods is user-based CF when using 10 synthetic datasets generated from DaGzang (0.437 at MAE and 0.465 at RMSE).
MSaD-Net: A Mix Self-Attention Networks for 3D Point Cloud Denoising

In the process of acquiring 3D point cloud data, due to environmental interference or unstable scanning equipment, the acquired data often have noisy points. Recently, with the development of neural networks for point clouds, great progress has been made in deep learning-based point cloud denoising. However, most of the existing methods adopt a pointnet-like structure to predict point offsets. Simple pooling operation loses much important information, such as local neighborhood information, and global information. The loss of information makes the algorithm ineffective when dealing with some complex cases. In order to solve the above problems, we propose a self-attention-based point cloud denoising network architecture, through the Transformer structure, to establish long-range dependencies of the points. In addition, we propose a local information embedding module to fast select meaningful points and serve as the input of the Transformer. We also consider the correlation between channels of point cloud features and further introduce a channel attention module. Extensive experiments prove that our method outperforms existing methods, and maintains a high running speed.
Reshoring and firm productivity

ABSTRACT This paper investigates the impact of reshoring, defined as the relocation of previously offshored production activities back to the home country or to a neighbouring country, on the productivity of manufacturing firms. It uses data from the European Reshoring Monitor on European firms in the period 2014–18. Reshoring increased total factor productivity of small and medium-sized firms, but did not affect productivity of large firms. The positive effect was stronger for firms that had offshored production to some Asian countries.
Comprehensive Analysis of the Expression, Prognostic Value, and Immune Infiltration Activities of GABRD in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is one of the tumors with the highest mortality rates. It is of the utmost significance to make an accurate prognostic assessment and to tailor one's treatment to the specific needs of the patient. Multiple lines of evidence point to the possibility that genetic variables and clinicopathological traits are connected to the onset and development of cancer. In the past, a number of studies have revealed that gamma-aminobutyric acid type A receptor subunit delta (GABRD) plays a role in the advancement of a number of different cancers. However, its function in COAD was rarely reported. In this study, we analyzed TCGA datasets and identified 29 survival-related differentially expressed genes (DEGs) in COAD patients. In particular, GABRD expression was noticeably elevated in COAD specimens. There was a correlation between high GABRD expression and an advanced clinical stage. According to the results of the survival tests, patients whose GABRD expression was high had a lower overall survival time and progression-free survival time than those whose GABRD expression was low. GABRD expression was found to be an independent predictive predictor for overall survival, as determined by multivariate COX regression analysis. Additionally, the predictive nomogram model can accurately predict the fate of individuals with COAD. In addition, we observed that GABRD expressions were positively associated with the expression of T cells regulatory (Tregs), macrophages M0, while negatively associated with the expression of T cells CD8, T cells follicular helper, macrophages M1, dendritic cells activated, eosinophils, and T cells CD4 memory activated. The IC50 of BI-2536, bleomycin, embelin, FR-180204, GW843682X, LY317615, NSC-207895, rTRAIL, and VX-11e was higher in the GABRD high-expression group. In conclusion, we have shown evidence that GABRD is a novel biomarker that is connected with immune cell infiltration in COAD and may be utilized to predict the prognosis of COAD patients.
A Deep Learning Approach for Sentiment Classification of COVID-19 Vaccination Tweets

Now-a-days, social media platforms enable people to continuously express their opinions and thoughts about different topics. Monitoring and analyzing the sentiments of people is essential for governments and business organizations to better understand people's feelings and thoughts. The Coronavirus disease 2019 (COVID-19) has been one of the most trending topics on social media over the last two years. Consequently, one of the preventative measures to control and prevent the spread of the virus was vaccination. A dataset was formed by collecting tweets from Twitter for over a month from November 13th to December 31st, 2021. After data cleaning, the tweets were assigned a positive, negative, or neutral label using a natural language processing (NLP) sentiment analysis tool. This study aims to analyze people's public opinion towards the vaccination process against COVID-19. To fulfil this goal, an ensemble model based on deep learning (LSTM-2BiGRU) is proposed that combines long short-term memory (LSTM) and bidirectional gated recurrent unit (BiGRU). The performance of the proposed model is compared to five traditional machine learning models, two deep learning models in addition to state-of-the-art models. By comparing the results of the models used in this study, the results reveal that the proposed model outperforms all the machine and deep learning models employed in this work with a 92.46% accuracy score. This study also shows that the number of tweets that involve neutral, positive, and negative sentiments is 517496 (37%) tweets, 484258 (34%) tweets, and 409570 (29%) tweets, respectively. The findings indicate that the number of people carrying neutral sentiments towards COVID-19 immunization through vaccines is the highest among others. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.
Polyomino: A 3D-SRAM-Centric Accelerator for Randomly Pruned Matrix Multiplication With Simple Reordering Algorithm and Efficient Compression Format in 180-nm CMOS

We have developed a sparse matrix reordering algorithm with a novel 3D-SRAM-centric Polyomino accelerator that enables efficient processing of the reordered matrix for parameter compression. By reordering randomly pruned, irregularly structured sparse matrices into regularly structured matrices, both the compression ratio of the data and the efficiency of the hardware processing increase. The reordering algorithm can be implemented simply by attributing it to the widely known k-sum problem. We also developed a compression format for storing the reordered matrices and show that the reordered regular structure can reduce the amount of required memory by 63% compared with the conventional method. The proposed Polyomino accelerator can efficiently process reordered matrices by using a 3D stacked SRAM, which is an external memory with random accessibility and low latency. The measurement results using a test chip fabricated in a 180-nm CMOS process demonstrate that the proposed accelerator can achieve high area-efficiency and high energy-efficiency and scales well with the pruning rate.