Miracle-dz/UniFAR-FTUs huggingface dataset:
Change mechanism of solver performance: With a special focus on time history analysis of supertall buildings

The correlation between the performance of some commonly used linear system solvers and the characteristics of system matrices, especially in the time history analysis of supertall buildings, is comprehensively identified. Relying on 5 well‐designed structural models with height varying from 47 to 660 m, the supernodal LLT solver and the conjugate gradient (CG) solver with the factorized sparse approximate inverse preconditioner are selected due to their actual efficiency. More importantly, basing on 90 fictitious supertall structural models generated from the parameterized method, the effects of structural characteristics on solver performance and the corresponding mechanism are studied by 3 mathematical indexes with parameters come from the equivalent dynamic stiffness matrices. Impacts of processors on the solvers are also clarified by associating the architectural characteristics of processors with the indexes. The performance of the 2 solvers is affected globally with increasing building size; different variation laws are shown for different structural systems. However, the accuracy of the CG solver does not exhibit distinct variation due to a slight change in the 2‐norm condition number of the matrices. The CG solver is found to be more applicable on GPU processor than the LLT solver due to its smaller gap between computational workload and the amount of data transferred.
How emotions from content social relevance mediate social media engagement: evidence from European supermarkets during the COVID-19 pandemic

PurposeThe purpose of this study is to examine the antecedents of consumer engagement with supermarkets' social media accounts. Drawing on regulatory fit theory and social sharing of emotions theory, the authors test if the content posted on the social media brand pages of supermarkets dealing with a topic of high social relevance, such as the COVID-19 pandemic, stimulates social media consumer engagement and if and how the engagement is mediated by the arousal of positive and negative emotions.Design/methodology/approachThe authors retrieved data from the Facebook accounts of the top 20 European supermarkets identified in the Deloitte 2020 Global Powers of Retailing report during the first wave of the pandemic from 1 March to 30 June 2020, collecting a sample of 2,524 posts from 8 different countries. After a content analysis to classify COVID-19 content, the authors applied the Baron and Kenny (1986) methodology to verify the hypothesised relationships.FindingsThe findings highlight a positive direct relationship between the social relevance of a topic (COVID-19) and social media consumer engagement mediated by the arousal of positive and negative emotions.Originality/valueTo the best of the authors’ knowledge, this is one of the earliest empirical research using Facebook data to investigate the role of the social relevance of content as an antecedent of social media consumer engagement with a specific focus on supermarkets. The paper contributes to the stream of social media literature investigating the antecedents of social media engagement behaviour, exploring the role of topics' choice and aroused emotions, which to date are both under-investigated.
An Optimized Feature Selection Method for E-Learning Recommender System Using Deep Neural Network based on Multilayer Perceptron

: An effective E-Learning system must identify learning content appropriate for the needs of the specific learner from among the many sources of learning content available. The learning content can consist of different types of media content namely Text, Video and Image and to determine a rating. Recommendation systems become increasingly used in information systems and especially in e-learning platform. These systems are used to propose and recommend content of these platforms to users according to needs of the later in order to allow them to have the maximum information for learning. In this paper, a recommendation system based on data mining and deep learning has been proposed to help the learner by recommending the appropriate learning content. To improve the recommendations a content matrix is created and based on this, the logistic regression and deep learning methods could classify recommendations. Experimental results show that the precision, recall and f measure for Top 5 & 10 Recommendations using Deep Neural Network performsby 1.439% better than Top 5 & 10 Recommendations using Logistic Regression among Chi Squared, SA, PSO and ICA methods.
Closed-Loop Simulation of Kelvin Probe Force Microscopy based on Reduced Finite Element Cantilever Modeling

In single-pass Kelvin Probe Force Microscopy (KPFM), the contact potential difference (CPD) and the topography of a sample are imaged simultaneously. The operation principle is based on a multifrequency excitation of the microcantilever. In this paper, a simulation model for singlepass KPFM is derived, with the topographical data obtained from frequency-mode atomic force microscopy (FM-AFM). The virtual KPFM contains all essential electronic components, e.g. demodulation stages and feedback controllers. The cantilever dynamics are accurately described by reduced finite element (FE) models. Simulation results demonstrate the operation principle of single-pass KPFM and the effects of uncompensated CPD on topography imaging.
Social capital, human capital, tacit knowledge, and innovations, A Polish-US cross-country study

This study measures the relationship between human and social capital (internal and external) and tacit knowledge sharing's influence on innovativeness among knowledge workers employed in Polish (n=1050) and US (n=1118) organizations. The structural equation modeling method revealed that internal social capital matters more for organizational innovativeness in the US. In Poland, both external and internal were important. Specifically, in the US, external social capital supports internal innovativeness directly and external - indirectly. On the contrary, in Poland, social capital directly influences internal and external innovativeness. These differences are partially due to country differences regarding public and private financing. Also apparent is the need for strong social bonds between employees in innovative companies. Furthermore, this study confirms that internal innovativeness is vital for external innovativeness and reveals that social capital is a structural enabler for innovativeness, given its critical role in the distribution of tacit knowledge.
Morpho-Taxonomic Study Based on Wild Weed Plant Species <i>Calotropis procera</i> (Aiton)—Whole Plant

This work is an attempt to make a taxonomic study on Calotropis procera plant in Tehama area, Al-Baha region of Saudi Arabia. Al-Baha Province is divided by huge and steep Rocky Mountains into two main sectors, a lowland coastal plain at the west, known as “Tihama” and a mountainous area with an elevation of (1500) to (2450) m above sea level at the east, known as “Al-Sarat or Al-Sarah” which form a part of Al-Sarawat Mountains range. The study included a scientific description of the family Apocynaceae and the scientific classification of the species Calotropis procera. Botanical names, synonyms and vernacular names (Local names) were presented. Botanical description is determined depending on its vegetative, floral and fruit characteristics. Notes on habitats and distribution were provided. The plant species has been illu-strated by photos in its habitat. Chemical components, medicinal properties and local uses were included. The study revealed that Calotropis procera is widely spread throughout the study area and it has great medicinal importance, so it requires more comprehensive studies for identification. The study aims to: (Number one is deleted). 1) Determine the distribution and habits of the species Calotropis procera. 2) Provide medicinal properties and local uses of Calotropis procera. The method was recorded due to Dr. Jacob Thomas, Herbarium Dept., College of Science, King Saud University. Authors recommend recent accurate techniques such as DNA fingerprinting to identify the species. More Biochemical studies on the effective chemical compounds are required for the medicinal importance of the plant.
Engineering of Leuconostoc citreum for Efficient Bioconversion of Soy Isoflavone Glycosides to Their Aglycone Forms

Soy isoflavones are phytochemicals that possess various beneficial physiological properties such as anti-aging, anti-tumor, and antioxidant properties. Since soy isoflavones exist in glycoside forms, their bioavailability requires initial hydrolysis of the sugar moieties bound to them to be efficiently absorbed through the gut epithelium. Instead of conventional chemical hydrolysis using acids or organic solvents, alternative strategies for enhancing the bioavailability of soy isoflavones using biological methods are gaining attention. Here, we engineered Leuconostoc citreum isolated from Korean kimchi for efficient bioconversion of soy isoflavone glycosides into their aglycone forms to enhance their bioavailability. We first constructed an expression module based on the isoflavone hydrolase (IH)-encoding gene of Bifidobacterium lactis, which mediates conversion of isoflavone glycosides to aglycone forms. Using a high copy number plasmid and bicistronic expression design, the IH was successfully synthesized in L. citreum. Additionally, we determined enzymatic activity of the IH using an in vivo β-glucosidase assay and confirmed its highly efficient bioconversion efficiency for various types of isoflavone glycosides. Finally, we successfully demonstrated that the engineered L. citreum could convert isoflavone glycosides present in fermented soymilk into aglycones.
BUES: A blockchain-based upgraded email system

This paper focuses on the improvement of traditional email system architecture with the help of blockchain technology in the existing network environment. The improved system architecture can better improve the security and stability of the system. The email content is extracted and stored in the blockchain network to achieve regulatory traceability between the email service provider and the higherlevel organization. In turn, A Blockchain-based Upgraded Email System(BUES) is proposed. The defects of the existing traditional email system are addressed. Firstly, the threat model of the traditional email system is analyzed, and solutions are proposed for various threats. Then a system architecture consisting of the blockchain network, email servers, and users are constructed. The implementation of BUES is carried out, and the related experimental process and algorithm steps are given. After the experimental analysis, it is shown that BUES can ensure the security, reliability, efficiency, and traceability of email transmission.
Application of the Synthetic Over-Sampling Method to Increase the Sensitivity of Algorithm Classification for Class Imbalance in Small Spatial Datasets

: The difficulty of acquiring data from numerous intergovernmental agencies/institutions for prone road traffic accidents (PRTA) spatial datasets produces a small-scale dataset that causes dataset imbalance. Class imbalance in small-scale datasets causes uncertainty in the results of the modeling PRTA classification. The proposed research is a scenario-based case representation model on the pre-processing data stage to increase the sensitivity of algorithm classification in a small-scale dataset that causes dataset imbalance using machine learning (ML), the synthetic over-sampling method. The retrieval of attributes from the spatial dataset is transformed into the raw dataset, the normalized dataset, the synthetic minority over-sampling technique (SMOTE) raw dataset, and SMOTE normalized dataset scenarios. Balancing datasets using four variants of SMOTE, namely ADASYN, Borderline-SMOTE, K-Means SMOTE, and SVM-SMOTE resampled. To evaluate how well the PRTA classification model performed, we utilized the hyper-parameters optimization technique and the genetic algorithm (GA) search cross-validation. The experiment was run with the ML classifier method, including the k-nearest neighbor (KNN), support vector machines (SVM), multilayer perceptron (MLP), naive bayes (NB), logistic regression (LR), and random forest (RF). The Area Under Curve (AUC) was used to evaluate the results of the experiments. The results of the dataset test in a predetermined scenario conclude that a single algorithm that is computationally light to produce an optimal classifier tends to use a raw dataset that is balanced using SMOTE. The KNN method as a single algorithm for classification based on the distance between samples is superior, with an AUC value of 0.89, which is included in the good classification category of all ML classifiers proposed to handle small data sets imbalanced classes using SMOTE raw datasets for K-Means variants SMOTE.
Comparison of Pedagogical and Professional Competencies of Certified and Uncertified Teachers in State Elementary Schools

This study's purpose is to know the pedagogical and professional competence of teachers who have education certificates and those who do not yet have education certificates, and to understand that the pedagogical and professional competence of certified teachers is higher than uncertified teachers at the public elementary school in Ajibarang District, Banyumas. This quantitative research was done at a state elementary school in Ajibarang District, Banyumas. The research sample was 92 teachers, divided by 46 who have certificates and 46 who have not had certificates. Data used were primary data which collecting by questionnaire distribution. Data analyzed by t-test independent sample. This research concludes that certified teachers have a high pedagogical competence, and the pedagogical competence of uncertified teachers is lower. The professional competence of certified teachers is high, and the professional competence of uncertified teachers is lower. Paedagogic and professional competence of certified teachers are better than uncertified ones at the public elementary school in Ajibarang District Banyumas.
Investigating the Interaction between Data and Algorithms

– Research in computer vision is centered on algorithmic improvements, for example, by developing better models. Thereby, the data is considered fixed. This is in contrast to many real-world applications of computer vision systems in which algorithms and data co-evolve. To address this shortcoming of previous research, we study the properties of the data and their interaction with deep learning algorithms. Thereby, we investigate the size of the data, the share of mislabels, class imbalance and the presence of unlabeled data which can be leveraged using semi-supervised learning. In experiments on 100 classes from ImageNet, we show that a tiny network architecture outperforms a much more powerful one it if has access to only a little bit more data. Only if vast amounts of data are available so that adding even more images has little effect on performance, large architectures dominate smaller ones. If little data is provided, adding a few labeled images has a huge effect on accuracy. Once accuracy saturates, massive amounts of additional data are needed to achieve even small improvements. Furthermore, we find that mislabels severely reduce performance. To fix that, we propose a cost-efficient way of identifying mislabels which is especially beneficial if many images are already available. Conversely, if little data is available, labeling more images is more advantageous than cleaning existing annotations. In the case of imbalanced data, we illustrate that labeling more instances from rare classes has a much greater effect on performance than only increasing dataset size. Moreover, we show that leveraging unlabeled images by semi-supervised learning offers a consistent benefit even if the labeled subset contains significant label noise.
Optimal Scheduling Model of WDM/OTN Network Transmission Line Based on Machine Learning

In order to solve the problem that the influencing factors are difficult to parameterize in the design and development of WDM/OTN backbone network routing planning tools, the author proposes an optimal scheduling model for WDM/OTN network transmission lines based on machine learning. Using the machine learning classification algorithm as a tool, the weight coefficients of each constraint factor are extracted from the historical design decisions, and the routing parameter model is constructed, so as to realize the intelligent routing selection, through actual simulation analysis and engineering verification. Simulation results show that after the historical routing regression test, the path coincidence rate of the route obtained by the algorithm and the historical real decision-making route reaches 81%, and the resource hit rate reaches 84%, which meets the requirements for actual production. Conclusion. This method can accurately and effectively generate network weight parameters so that the software routing is more intelligent.
Research on the Mechanism of Multi-Domain Coupling Centrifugal Electrostatic Blowing Flying Deposition

The centrifugal electrostatic blowing process proposed in this paper solves the difficult continuous and stable deposition problem in the traditional centrifugal electrostatic spinning process. By establishing a flight deposition model of the centrifugal electrostatic spraying process, CFD is used to simulate and analyze the electrohydrodynamic effect of centrifugal jets, and the driving mechanism is explored. Subsequently, MATLAB is used to obtain the optimal solution conditions, and finally, the establishment of a two-dimensional flight trajectory model is completed and experimentally verified. In addition, the deposition model of the jet is established to clarify the flight trajectory under the multi-field coupling, the stable draft area of the jet is found according to this, and the optimal drafting station is clarified. This research provides new ideas and references for the exploration of the deposition mechanism of the centrifugal electrostatic blowing and electrostatic spinning process.
Base Paper on Estimation of Vehicle Orientation Using Stereo Vision

: Nowadays, image processing is among rapidly growing technologies. Image processing basically includes the following three steps: Importing the image via image acquisition tools, Analyzing and manipulating the image, Output in which result can be altered image or report that is based on image analysis. In this paper we are going to estimate the pose of vehicle with stereovision. With stereo vision, we can see where things are in relation to our own bodies with much greater harshness --especially when those objects are moving toward or away from us in the depth component. Stereoscopic vision is what gives us the ability to see things with height, width, and depth.The estimation of attitude of a vehicle closely depends on the 3D correspondences or triangulation. This implies obtaining the correct estimate of the 3D point in space, which are used to obtain the correct estimate of the attitude. Hence an effort was made in order to minimize the triangulation error further. A video sequence from a stereoscopic system, image features are detected and matched for the initial stereo pair, then reconstructed into 3D space and stored as 3D landmarks after outlier elimination. After that key features are tracked in the subsequent images and used to estimate the camera motion. Considering the two algorithms for which the optimization is performed, it has been found that the triangulation is far better than the disparity method. Observing the results, we may conclude that the problem of local minima has affected the output that has led to the convergence of the method onto some other point.
Advancing urban regeneration projects for sustainable development and intellectual capital

The study aims to investigate how urban regeneration projects contribute to achieving sustainable development through an intellectual capital (IC) perspective for driving growth within urban communities and improving quality of life. Literature has shown that urban regeneration projects help support urban change. The IC helps drive sustainable development and competitiveness of regions, cities and communities. Urban regeneration projects contribute to better quality of life and drive social and economic renewal of urban communities. This exploratory study proposes a research framework addressed to join urban regeneration projects with IC view on cities and urban communities. This paper can contribute to the extant literature focused on the relationship between urban regeneration processes and the role of intellectual capital as driver of sustainable development. This qualitative study, using a multiple case study methodology, provides a comparative analysis of two urban regeneration projects that help the redevelopment of urban environments and support human and social capital within communities. The findings show that urban regeneration projects significantly contribute to revitalising urban spaces, supporting the components of IC that drive social and economic growth of communities.
Effects of Heating, Pelleting, and Feed Matrix on Apparent Concentrations of Cereal Ergot Alkaloids in Relation to Growth Performance and Welfare Parameters of Backgrounding Beef Steers

As the contamination of cereal grains with ergot has been increasing in Western Canada, studies were undertaken to evaluate the impacts of heating (60, 80, 120, or 190 °C) alone or in combination with pelleting on concentrations of ergot alkaloids. Fifteen samples of ergot-contaminated grain from Alberta and Saskatchewan were assayed for R and S epimers of six alkaloids (ergocryptine, ergocristine, ergocornine, ergometrine, ergosine, and ergotamine) using HPLC MS/MS. Five samples with distinct alkaloid profiles were then selected for heating and pelleting studies. Heating resulted in a linear increase (p < 0.05) of total R and total S epimers with increasing temperature, although some individual R epimers were stable (ergometrine, ergosine, ergotamine). Pelleting also increased (p < 0.05) concentrations of total R and total S epimers detected, although ergometrine concentration decreased (p < 0.05) after pelleting. A feeding study arranged in a 2 × 2 factorial structure used 48 backgrounding Angus-cross steers fed four different diets: (1) Control Mash (CM, no added ergot), (2) Control Pellet (CP), (3) Ergot Mash (EM), or (4) Ergot Pellet (EP). Pelleting heated the ergot to 90–100 °C under 4 bars pressure, but the ergot used in the feeding study was not otherwise heated. Alkaloid concentrations of EM and EP varied by up to 1.1 mg/kg depending on the feed matrix assayed. No differences among treatments were noted for growth performance, feed intake, feed conversion, concentrations of serum prolactin and haptoglobin, hair cortisol, or in temperatures of extremities measured by infrared thermography. The only negative impacts of ergot alkaloids were on blood parameters indicative of reduced immune function or chronic inflammation. Pelleting did not heighten the negative clinical outcomes of ergot, although alkaloid concentrations of pelleted feed increased depending on the matrix assayed. It was hypothesized that the heat and pressure associated with pelleting may enhance the recovery of alkaloids from pelleted feed.