The availability of cheap and widely applicable person identification techniques is essential due to a wide-spread usage of online services. The dynamics of typing is characteristic to particular users, and users are hardly able to mimic the dynamics of typing of others. State-of-the-art solutions for person identification from the dynamics of typing are based on machine learning. The presence of hubs, i.e., few instances that appear as nearest neighbours of surprisingly many other instances, have been observed in various domains recently and hubness-aware machine learning approaches have been shown to work well in those domains. However, hubness has not been studied in the context of person identification yet, and hubnessaware techniques have not been applied to this task. In this paper, we examine hubness in typing data and propose to use ECkNN, a recent hubness-aware regression technique together with dynamic time warping for person identification. We collected time-series data describing the dynamics of typing and used it to evaluate our approach. Experimental results show that hubness-aware techniques outperform state-of-the-art time-series classifiers.

We present a novel quantum algorithm for the classification of images. The algorithm is constructed using principal component analysis and von Neuman quantum measurements. In order to apply the algorithm we present a new quantum representation of grayscale images.

Studies based on the most common diagnostic categories do not bring conclusive results concerning the overlapping and distinctive features of anxiety and depression, especially in the areas of attentional functioning, structure of affect, and cognitive emotion regulation. However, a new typology has been proposed which treats anxiety and depression as personality types (Fajkowska, 2013). These types – arousal and apprehension anxiety as well as valence and anhedonic depression – are constructed based on two criteria: specific structure and functions (reactive or regulative). The present paper critically examines the empirical evidence related to this approach. The data mostly confirmed the prediction that the similarities and differences in attentional and affective functioning among the anxiety and depression types would be related to their shared and specific structural and functional characteristics. The new typology turned out to be suitable for integrating the existing research findings by relating them to the structure and functions of anxiety and depression. As a result, it is useful in explaining some of the inconsistencies in literature, as it allows to identify the overlapping and distinctive features of the anxiety and depression types. It also helps to understand the mechanisms contributing to the development and maintenance of anxiety and depression, which might be useful in diagnosis and treatment. However, even though Fajkowska’s approach is an important contribution to the understanding of anxiety and depression, it is not exhaustive. Its limitations are discussed, along with proposed modifications of the theory, as well as further research directions.

Sugar beet ( Beta vulgaris L.) has emerged as an alternative to sugarcane. It is mainly utilized for sugar extraction and has significant industrial value with great nutritional impact. Different kinds of biotic and abiotic stresses are considered to be major barriers for sugar beet cultivation. As per the current scenario, every year sugar beet production suffers huge yield losses due to various stresses. The conventional breeding technique is a time-consuming lengthy procedure which can be replaced by a genetic transformation technique to bring new transgenic traits within a short period of time. Sugar beet has proven to be excellent sample material for in vitro culture of haploid plants, protoplast culture, somaclonal variation, and single cell culture, among others. Agrobacterium mediated and PEG-mediated transformations are the most effective genomic transformations in the case of sugar beet. Development of new traits in terms of fungus/virus, pest/nematode tolerance, herbicide and salt tolerance are the most frequently expected traits in the current scenario of sugar beet production. Potential transgenic plants are viable alternatives to traditional expression systems for end product (protein) development with more accuracy. So, transgenic production through genome editing/base editing is presently considered to be one of the best tools for sugar beet tolerant traits development. Food safety and environmental impacts are two major concerns of genetic transformation in sugar beet and need to be appropriately screened for public health acceptability.

Analysis of groundwater quality in the alluvial aquifer of the lower Soummam Valley, North-East of Algeria, was realised through the application of multivariate statistical methods: hierarchical cluster analysis (HCA) in Q and R modes, factorial correspondence analysis (FCA), and principal component analysis (PCA), to hydrochemical data from 51 groundwater samples, collected from 17 boreholes during periods of June, September 2016 and March 2017. The objectives of this approach are to characterise the water quality and to know the factors which govern its evolution by processes controlling its chemical composition. The Piper diagram shows two hydrochemical facies: calcium chloride and sodium bicarbonate. Statistical techniques HCA, PCA, and FCA reveal two groups of waters: the first (EC, Ca2+, Mg2+, Cl–, SO42– and NO3–) of evaporitic origin linked to the dissolution processes of limestone rocks, leaching of saliferous soils and anthropogenic processes, namely contamination wastewater and agricultural activity, as well marine intrusion; and the second group (Na+, K+, and HCO3–) of carbonated origin influenced by the dissolution of carbonate formations and the exchange of bases. The thermodynamic study has shown that all groundwater is undersaturated with respect to evaporitic minerals. On the other hand, it is supersaturated with respect to carbonate minerals, except for water from boreholes F9, F14, and F16, which possibly comes down to the lack of dissolution and arrival of these minerals. The results of this study clearly demonstrate the utility of multivariate statistical methods in the analysis of groundwater quality.