Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Hypericum accessions by DNA fingerprinting and flow cytometry

Hypericum perforatum, H. umbellatum, H. maculatum, and H. hircinum accessions originating from botanical gardens across Europe were examined by flow cytometry and molecular markers. 2C DNA content of 17 Hypericum perforatum accessions (Hp) and the H. perforatum cultivar Topaz amounted to between 1.56 pg and 1.62 pg. In four Hp accessions some individual plants were found with a DNA content corresponding to 6Cx (2.34 - 2.39 pg). All plants of accession Hp8 showed a DNA content of 6Cx (2.41 pg). In root tips of Hp plants with an average DNA amount of 1.58 pg, 32 chromosomes were detected, corresponding to 2n = 4x. This is the first ploidy and/or DNA content report for H. umbellatum, H. maculatum and H. hircinum. H. umbellatum and H. maculatum, each contained 0.76 pg DNA and 16 chromosomes were counted. The 2C DNA content of H. hircinum was 1.00 pg with the best metaphase plate revealing 32 chromosomes. Additionally, a combined marker analysis, based on inter-simple sequence repeats (ISSR) and sequence related amplified polymorphism (SRAP), was conducted to gain a better understanding of diversity especially within the accessions of H. perforatum. A total of 27 (11 ISSR and 16 SRAP) primer combinations were screened, showing 699 bands, of which 661 were polymorphic. UPGMA clustering revealed that accessions from the same geographic area tended to be more closely related, while H. maculatum was grouped separately from all H. perforatum accessions. Both methods have shown similar sensitivities in detecting the genetic diversity of the analyzed genotypes. Our results may be useful for Hypericum breeding programs and the development of effective conservation strategies.
Raking and Relabeling for Imbalanced Data

We consider the binary classification of imbalanced data. A dataset is imbalanced if the proportion of classes are heavily skewed. Imbalanced data classification is often challengeable, especially for high-dimensional data, because unequal classes deteriorate classifier performance. Under sampling the majority class or oversampling the minority class are popular methods to construct balanced samples, facilitating classification performance improvement. However, many existing sampling methods cannot be easily extended to high-dimensional data and mixed data, including categorical variables, because they often require approximating the attribute distributions, which becomes another critical issue. In this paper, we propose a new sampling strategy employing raking and relabeling procedures, such that the attribute values of the majority class are imputed for the values of the minority class in the construction of balanced samples. The proposed algorithms produce comparable performance as existing popular methods but are more flexible regarding the data shape and attribute size. The sampling algorithm is attractive in practice, considering that it does not require density estimation for synthetic data generation in oversampling and is not bothered by mixed-type variables. In addition, the proposed sampling strategy is robust to classifiers in the sense that classification performance is not sensitive to choosing the classifiers.
Application of wavelet packet method in frequency band energy distribution of rock acoustic emission signals under impact loading

Based on the character of non-stationary signal,the feature of energy distribution in acoustic emission signals under impact loading was investigated by means of the wavelet packet method.Firstly,the characters of wavelet packet analysis were introduced.Secondly,eight signals were analyzed by using wavelet packet based on software MatLab and the change of energy distribution curve in different frequency bands was obtained.Finally,the law of energy distribution of acoustic emission signals under impact loading was analyzed,and the influence of different rocks on frequency band energy distribution of acoustic emission signals under impact loading was principally explored.The results showed that frequency band energy distribution of acoustic emission signals under impact loading is affected by different rocks,in other words,if the density of rock is less,the speed of the longitudinal wave is lower;and the elasticity modulus is less,the dominant frequency bands of acoustic emission signals under impact loading become more energy-intensive and tend to be lower;uniaxial compression strength and tensile strength have nothing to do with the dominant frequency bands of acoustic emission signals under impact loading.
Social Networks Fake Account and Fake News Identification with Reliable Deep Learning

Recent developments of the World Wide Web (WWW) and social networking (Twitter, Instagram, etc.) paves way for data sharing which has never been observed in the human history before. A major security issue in this network is the creation of fake accounts. In addition, the automatic classification of the text article as true or fake is also a crucial process. The ineffectiveness of humans in distinguishing the true and false information exposes the fake news as a risk to credibility, democracy, logical truth, and journalism in government sectors. Besides, the automatic fake news or rumors from the social networking sites is a major research area in the field of social media analytics. With this motivation, this paper develops a new reliable deep learning (DL) based fake account and fake news detection (RDL-FAFND) model for the social networking sites. The goal of the RDL-FAFND model is to resolve the major problems involved in the social media platforms namely fake accounts, fake news/rumor identification. The presented RDL-FAFND model detects the fake account by the use of a parameter tuned deep stacked Auto encoder (DSAE) using the krill herd (KH) optimization algorithm for detecting the fake social networking accounts. Besides, the presented RDL-FAFND model involves an ensemble of the machine learning (ML) models with different linguistic features (EML-LF) for categorizing the text as true or fake. An extensive set of experiments have been carried out for highlighting the superior performance of the RDL-FAFND model. A detailed comparative results analysis has stated that the presented RDL-FAFND model is considerably better than the existing methods.
Social prescribing and behaviour change: proposal of a new behaviour change technique concerning the ‘connection’ step

Social prescribing, also known as community referral, is gaining international recognition as a health and social care initiative with benefits for individuals (e.g. improving mental wellbeing), healthcare services (e.g. decreasing inpatient admissions), and societies (e.g. creating more inclusive communities) (Drinkwater, Wildman, & Moffatt, 2019; Global Social Prescribing Alliance, 2021). Further, it is being advocated as an advantageous tool to help facilitate recovery from the COVID-19 pandemic (Royal College of Psychiatrists, 2021). Social prescribing involves a health or social care professional connecting an individual with an appropriate community-based opportunity to improve that individual’s health (physical and/or mental) and wellbeing (physical, mental and/or social), for example, a local jogging group to increase physical activity levels, or a hobby club to reduce feelings of loneliness. The connection can be made via a direct route (health or social care professional connecting person to communitybased opportunity) or an indirect route (health or social care professional connecting person to social prescribing professional – usually referred to as ‘link worker’ or ‘community connector’ – and social prescribing professional connecting person to communitybased opportunity). Different methods of connection can be utilised: signposting, prescription or referral in a direct route, and a combination of these methods in an indirect route (Cunningham, Rogowsky, Carstairs, Sullivan, & Ozakinci, 2021). Social prescribing can therefore be conceptualised as a ‘system’ (Husk, Elston, Gradinger, Callaghan, & Asthana, 2019, p. 7) comprising two parts: (1) the community-based opportunities for health and wellbeing improvement; (2) the processes of connection from health or social care to those community-based opportunities (Cunningham et al., 2021; Husk et al., 2019).
Strong Tracking Filter Based Fundamental Phase Real-time Extraction for Single-phase Voltage

Fundamental frequency and instantaneous phase of the grid voltage are the critical information for the operation of the grid-connected power generating systems.A novel strong tracking filter(STF) based real-time extraction method of the single-phase grid voltage fundamental phase was proposed.The grid voltage fundamental frequency and DC offset were added as two of the state variables,and the nonlinear state space description of the single-phase voltage signal contaminated by DC offset and harmonics was established.Considering the extended Kalman filter(EKF) can’t fast track the system parameters mutation when the system reaches a steady state,STF was introduced to estimate the state variables to get the fundamental frequency,amplitude and phase of the single-phase grid voltage signal in real-time.Experimental results prove the proposed phase real-time extracting method can eliminate the impact of DC offset and fast track the power parameters mutation.
Association of Hypokalemia Incidence and Better Treatment Response in NSCLC Patients: A Meta-Analysis and Systematic Review on Anti-EGFR Targeted Therapy Clinical Trials

Background This meta-analysis was designed to explore the relationship between the level of serum potassium and the treatment effect of epidermal growth factor receptor (EGFR) antagonist in advanced non-small cell lung cancer (aNSCLC). Methods We searched phase II/III prospective clinical trials on treatment with EGFR antagonists for aNSCLC patients. The objective response rate (ORR) and/or the disease control rate (DCR) and the incidence of hypokalemia of high grade (equal to or greater than grade 3) were summarized from all eligible trials. Heterogeneity, which was evaluated by Cochran’s Q-test and the I 2 statistics, was used to determine whether a random effects model or a fixed effects model will be used to calculate pooled proportions. Subgroup analysis was performed on different interventions, line types, phases, and drug numbers. Results From 666 potentially relevant articles, 36 clinical trials with a total of 9,761 participants were included in this meta-analysis. The pooled ORR was 16.25% (95%CI = 12.45–21.19) when the incidence of hypokalemia was 0%–5%, and it increased to 34.58% (95%CI = 24.09–45.07) when the incidence of hypokalemia was greater than 5%. The pooled DCR were 56.03% (95%CI = 45.03–67.03) and 64.38% (95%CI = 48.60–80.17) when the incidence rates of hypokalemia were 0%–5% and greater than 5%, respectively. The results of the subgroup analysis were consistent with the results of the whole population, except for not first-line treatment, which may have been confounded by malnutrition or poor quality of life in long-term survival. Conclusion The efficacy of anti-EGFR targeted therapy was positively associated with the hypokalemia incidence rate. Treatment effects on the different serum potassium strata need to be considered in future clinical trials with targeted therapy.
Acquisition of Croatian prepositional phrases by mono- and bilingual
 children

Children acquire prepositional phrases later than content words during language acquisition. This paper aims to describe the acquisition of prepositional phrases in Croatian, focusing on the order of their acquisition and the influence of their frequency in child-directed speech. To gain insight into the early acquisition of prepositional phrases, the longitudinal spontaneous child speech (CS) and child-directed speech (CDS) of three monolingual children were analysed, as well as a smaller sample of CS and CDS from four bilingual children. The results suggest that the acquisition of prepositional phrases is similar for monolingual and bilingual speakers, and that it depends on how often the children hear the prepositional phrases. Our findings generally support the idea that language development in bilingual children is similar to that of monolingual children, but that an analysis of a larger sample of bilingual children is needed.
Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System

The increasing user of Internet has witnessed a continued exploration in applications and services that can bring more convenience in people's life than ever before. At the same time, with the exploration of online services, the people face unprecedented difficulty in selecting the most relevant service on the fly. In this context, the need for recommendation system is of paramount importance especially in helping the users to improve their experience with best value-added service. But, most of the traditional techniques including collaborative filtering (CF) which is one of the most successful recommendation technique suffer from two inherent issues namely, rating sparsity and cold-start. Inspired by the breakthrough results and great strides of deep learning in a wide variety of applications, this work for the first time proposes a deep clustering model leveraging the strength of deep learning and fuzzy clustering for user-based collaborative filtering (UCF). At first, the proposed model applies deep autoencoder (AE) to learn users’ latent feature representation from the original rating matrix and then conducts fuzzy clustering assignments on learnt users’ latent representation to form user clusters. More importantly, the proposed model defines a unified objective function combining the reconstruction loss and clustering loss to yield end-toend UCF process that can jointly optimize the process of data representation learning and clustering assignment. The effectiveness of the proposed model is evaluated with two real-world datasets, MoiveLens100K and MovieLens1M using two standard performance metrics. Experimental results of the proposed model demonstrated the prospects for gain in top-K recommendation accuracy against recent related works with resistant to rating sparsity and cold start problems.
Exploring the potent inhibitor β-stigmasterol from Pittosporum dasycaulon Miq. leaves against snake venom phospholipase A2 protein through in vitro and molecular dynamics behavior approach

Abstract β-Stigmasterol, responsible for antivenom activity against abundant PLA2 protein of Viper (Daboia russelii) and Cobra (Naja naja) venom, was identified, characterized, and isolated from Pittosporum dasycaulon leaves extract. The compound purity was checked by RP-HPLC analysis, and structure was elucidated by FTIR and NMR analysis, followed by in-vitro as well as in-silico studies. The structural stability of the docked complexes was evaluated by molecular dynamic simulation, and the binding free energies were calculated by MM-PBSA analysis. The in-vitro analysis revealed that the isolated compound β-SS is effective against D. russelii PLA2 (IC50 40.903 ± 0.479 μg/mL) than N. naja venom PLA2 (42.340 ± 0.11 μg/mL). The in-silico approaches helps to conclude that the β-SS-PLA2 D. russelii complex showed a stable conformation with a reduced degree of flexibility throughout the dynamics simulation time period with high-affinity docking energy −10.60 kcal/mol than β-SS-PLA2 N. naja complex, which observed docking energy of −10.39 kcal/mol.
Grey Smoothing Model for Predicting Mine Gas Emission

A grey smoothing model for predicting mine gas emission was presented by combining the grey system theory with the smoothing prediction technique. First of all, according to the variable sequence, GM(1,1) model was set up to predict the general development trend of variable as first fitted values, then the smoothing prediction technique was used to revise the fitted values so as to improve the accuracy of prediction. The results of application in the No.6 Coal Mine in Pingdingshan mining area show that the grey smoothing model has higher accuracy than that of GM(1,1) in predicting the variable sequence with strong fluctuation. The research provides a new scientific method for predicting mine gas emission.
Streaming Regular Expression Membership and Pattern Matching

Regular expression search is a key primitive in myriads of applications, from web scrapping to bioinformatics. A regular expression is a formalism for compactly describing a set of strings, built recursively from single characters using three operators: concatenation, union, and Kleene star. Two basic algorithmic problems concerning such expressions are membership and pattern matching. In the regular expression membership problem, we are given a regular expression R and a string T of length n , and must decide whether T matches R . In the regular expression pattern matching problem, the task to ﬁnd the substrings of T that match R . By now we have a good understanding of the complexity of regular expression membership and pattern matching in the classical setting. However, only some special cases have been considered in the practically relevant streaming setting: dictionary matching and wildcard pattern matching. In the dictionary matching problem, we are given a dictionary of d strings of length at most m and a string T , and must ﬁnd substrings of T that match one of the dictionary strings. In the wildcard pattern matching problem, we are given a string P of length m that contains d wildcards, where a wildcard is a special symbol that matches any character of the alphabet, and a string T , and must ﬁnd all substrings of T that match P . Both problems can be solved in the streaming model by a randomised Monte Carlo algorithm that uses O ( d log m ) space [Golan and Porat (ESA 2017), Golan, Kopelowitz and Porat (Algorithmica 2019)]. In the general case, we cannot hope for a streaming algorithm with space complexity smaller than the length of R for either variant of regular expression search. The main contribution of this paper is that we identify the number of unions and Kleene stars, denoted by d , as the parameter that allows for an eﬃcient streaming algorithm. This parameter has been previously considered in the classical setting, and it has been observed that in practice it is signiﬁcantly smaller than the length of R . We design general randomised Monte Carlo algorithms for both problems that use O ( d 3 polylog n ) space in the streaming setting. A crucial technical ingredient of our algorithms is an adaptation of the general framework for evaluating a circuit with addition and convolution gates in a space-eﬃcient manner [Lokshtanov and Nederlof (STOC 2010), Bringmann (SODA 2017)], initially designed as a key component of a pseudopolynomial time algorithm for the subset sum problem. We show how to replace the Extended Generalised Riemann Hypothesis in [Bringmann (SODA 2017)] by an application of the Bombieri–Vinogradov theorem to achieve the same bounds (but unconditionally), which might be of independent interest.
Dynamic analysis of deep-ocean mining system based on multi-rigid-body discrete element model

Deep-ocean mining pipe is up to several thousands meters long and is of geometrical nonlinear characteristics of large displacement and small deformation.The multi-rigid-body discrete element method was proposed for the analysis of deep-ocean mining pipe system.Firstly,two examples,which belong to static analysis and dynamic analysis respectively,were given to show the effectiveness of discrete element method.Then the discrete element method was used to perform dynamic analysis of the Chinese 1 km deep-ocean mining system.The dynamic performance of the entire mining system under different current directions was discussed and compared with that from finite element method.The simulation results show that for long pipeline system,compared with finite element method,the computation speed of the present discrete element method is increased by about 8 times under the equivalent precision.
Visegrad Group countries as a junkyard of Europe? Quality of food and beverages as a theme of the 2019 European Parliament election campaigns – the case of Czechia

The so-called dual food quality played a very important role in the Visegrad Group countries, including in the local European Parliament election campaigns. Therefore, it is also addressed in this study, which focuses primarily on the legal and political science aspects thereof. The main research questions that I try to answer are whether it is legal under European law to market a product with the same or similar packaging with different ingredients and what role dual food quality played in the campaigns of political parties in the Visegrad Group countries during the European Parliament elections. In the introduction, I define the concept of food quality as well as the so-called dual food quality, which both European and Member States’ legislation (including the Visegrad Group countries) are only getting acquainted with. In the main chapters, I deal with the development of legislation on dual quality at the EU level, listing concrete steps taken by the EU within the common market and analysing the campaigns of Czech political parties in the European Parliament election as a case study. In addition, the programmes of parties standing for election and their other public presentations in the campaign are analysed.
Neural factoid geospatial question answering

Existing question answering systems struggle to answer factoid questions when geospatial information is involved. This is because most systems cannot accurately detect the geospatial semantic elements from the natural language questions, or capture the semantic relationships between those elements. In this paper, we propose a geospatial semantic encoding schema and a semantic graph representation which captures the semantic relations and dependencies in geospatial questions. We demonstrate that our proposed graph representation approach aids in the translation from natural language to a formal, executable expression in a query language. To decrease the need for people to provide explanatory information as part of their question and make the translation fully automatic, we treat the semantic encoding of the question as a sequential tagging task, and the graph generation of the query as a semantic dependency parsing task. We apply neural network approaches to automatically encode the geospatial questions into spatial semantic graph representations. Compared with current template-based approaches, our method generalises to a broader range of questions, including those with complex syntax and semantics. Our proposed approach achieves better results on GeoData201 than existing methods.