Minarets, tall structures, connected or not to the mosque attract attention due to their specific architectural features. Vulnerability to seismic damage has been witnessed throughout history on tall and slender structures after earthquake ground motions. In that respect, it is of the utmost importance to investigate the dynamic characteristics and resilience of historical stone minarets. This paper aims to provide the results of an on-site dynamic investigation of a stone minaret in Mostar and deliver its seismic assessment. The minaret is part of the Tabačica mosque built at the turn of the 16th and 17th century in the City of Mostar, Bosnia and Herzegovina. The on-site investigation comprised dynamic identification of the minaret by ambient vibration testing and qualitative estimation of the masonry wall by sonic pulse velocity testing. Besides the modal analysis a time-history analysis was performed by using the Applied Element Method (AEM), considered an appropriate tool for assessing the behavior of historic masonry structures. A good match is found between the first natural frequency obtained by the on-site investigation and the modal analysis which is a solid basis for further seismic assessment of the minaret as a slender tower-like structure. The concentration of stresses is observed at the transition zones.

The most significant bone used in taxonomic research is the skull. This study attempted to identify differences between the three cat species by measuring the skulls of each using computed tomography sections. The study used a total of 32 cat skulls, including 16 Van Cats, eight British Shorthairs and eight Scottish Folds. Van Cat had the highest values for cranial and skull length, whereas British Shorthair had the lowest values. The difference between the British Shorthair and Scottish Fold skull length and cranial length measures was not statistically significant. However, the Van Cat skull length result was statistically different from other species (p < 0.05). Scottish Fold had the broadest head (cranial width: 41.02 ± 0.79 mm). These results demonstrated that the scull of the Van Cat was longer but thinner than that of other species. In comparison to other species, the form of the Scottish Fold skull was more rounded. Internal height of cranium measurements for Van Cat and British Shorthair were statistically significant. In Van Cats, this measurement was 27.81 ± 1.58 mm, while in British Shorthairs, it was 30.23 ± 1.89 mm. Measurements of the foremen magnum were not statistically significant for any species. Van Cat's measures for the foramen magnum were the highest (Foramen magnum height: 11.59 ± 0.93 mm; Foramen magnum width: 14.18 ± 0.70 mm). Scottish Fold had the highest cranial index (Cranial index: 55.50 ± 4.02). Van Cat had the lowest value for this (Cranial index: 50.19 ± 2.16). Van Cat's cranial index value differed statistically from that of other species (p < 0.05). Between species, the foramen magnum index was not significant. None of the index values were statistically significant for Scottish Fold and British Shorthair. Foramen magnum width had the highest age‐to‐measurement value correlation (r = 0.310), although it was statistically insignificant. Skull length had the highest weight‐to‐measurement value correlations (R = 0.809), and it proved to be statistically significant. Skull length was the measuring value that distinguished male and female the most clearly (p = 0.000).

Restoring damaged microbiomes is still a formidable challenge. Classical widely adopted approaches consist of augmenting communities with pure or mixed cultures in the hope that these display their intended selected properties under in situ conditions. ABSTRACT Strain inoculation (bioaugmentation) is a potentially useful technology to provide microbiomes with new functionalities. However, there is limited understanding of the genetic factors contributing to successful establishment of inoculants. This work aimed to characterize the genes implicated in proliferation of the monoaromatic compound-degrading Pseudomonas veronii 1YdBTEX2 in nonsterile polluted soils. We generated two independent mutant libraries by random minitransposon-delivered marker insertion followed by deep sequencing (Tn-seq) with a total of 5.0 × 105 unique insertions. Libraries were grown in multiple successive cycles for up to 50 generations either in batch liquid medium or in two types of soil microcosms with different resident microbial content (sand or silt) in the presence of toluene. Analysis of gene insertion abundances at different time points (passed generations of metapopulation growth), in comparison to proportions at start and to in silico generated randomized insertion distributions, allowed to define ~800 essential genes common to both libraries and ~2,700 genes with conditional fitness effects in either liquid or soil (195 of which resulted in fitness gain). Conditional fitness genes largely overlapped among all growth conditions but affected approximately twice as many functions in liquid than in soil. This indicates soil to be a more promiscuous environment for mutant growth, probably because of additional nutrient availability. Commonly depleted genes covered a wide range of biological functions and metabolic pathways, such as inorganic ion transport, fatty acid metabolism, amino acid biosynthesis, or nucleotide and cofactor metabolism. Only sparse gene sets were uncovered whose insertion caused fitness decrease exclusive for soils, which were different between silt and sand. Despite detectable higher resident bacteria and potential protist predatory counts in silt, we were, therefore, unable to detect any immediately obvious candidate genes affecting P. veronii biological competitiveness. In contrast to liquid growth conditions, mutants inactivating flagella biosynthesis and motility consistently gained strong fitness advantage in soils and displayed higher growth rates than wild type. In conclusion, although many gene functions were found to be important for growth in soils, most of these are not specific as they affect growth in liquid minimal medium more in general. This indicates that P. veronii does not need major metabolic reprogramming for proliferation in soil with accessible carbon and generally favorable growth conditions. IMPORTANCE Restoring damaged microbiomes is still a formidable challenge. Classical widely adopted approaches consist of augmenting communities with pure or mixed cultures in the hope that these display their intended selected properties under in situ conditions. Ecological theory, however, dictates that introduction of a nonresident microbe is unlikely to lead to its successful proliferation in a foreign system such as a soil microbiome. In an effort to study this systematically, we used random transposon insertion scanning to identify genes and possibly, metabolic subsystems, that are crucial for growth and survival of a bacterial inoculant (Pseudomonas veronii) for targeted degradation of monoaromatic compounds in contaminated nonsterile soils. Our results indicate that although many gene functions are important for proliferation in soil, they are general factors for growth and not exclusive for soil. In other words, P. veronii is a generalist that is not a priori hindered by the soil for its proliferation and would make a good bioaugmentation candidate.

Aim: Comparison of accuracy rate in prediction of cardiovascular disease using Naive Bayes with Logistic Regression. Materials and Methods: The Naive Bayes (N=10) and Logistic Regression Algorithm (N=10) these two algorithms are calculated by using 2 Groups and taken 20 samples for both algorithm and accuracy in this work. The sample size is determined using the G power Calculator and it’s found to be 10. Results: Based on the Results Accuracy obtained in terms of accuracy is identified by Naive Bayes (87.02%) over the Logistic Regression algorithm (92.18%). Statistical significance difference between novel Naive Bayes algorithm and Logistic Regression Algorithm was found to be p=0.001 (2 tailed) (p<0.05). Conclusion: Prediction of cardiovascular disease using Logistic Regression is significantly better than the Naive Bayes.

Credit scoring is one the most important parts of credit risk management in reducing the risk of client defaults and bankruptcies. Deep learning has received much attention in recent years, but it has not been implemented so intensively in credit scoring compared to other financial domains. In this article, stacked unidirectional and bidirectional LSTM (long short‐term memory) networks as a complex area of deep learning are applied in solving credit scoring problems for the first time. The proposed robust model exploits the full potential of the three‐layer stacked LSTM and BDLSTM (bidirectional LSTM) architecture with the treatment and modeling of public datasets in a novel way since credit scoring is not a time sequence problem. Attributes of each loan instance were transformed into a sequence of the matrix with a fixed sliding window approach with a one‐time step. Our proposed models outperform existing and much more complex deep learning solutions thus we succeeded in preserving simplicity. In this article, measures of different types are employed to carry out consistent conclusions. The results by applying three hidden layers on the German Credit dataset showed an accuracy of 87.19%, for Kaggle dataset accuracy reached 93.69%, and for Microcredit dataset accuracy of 97.80%.

This study investigates the indirect mechanisms relevant to converting young adults' prosocial attitudes and individual responsibility into their prosocial behavior. Our results are based on a sample of 530 young adults studying at three public regional business schools in South East Europe. They show a significant favorable influence on young adults' civic and political involvement, mediating the relationship between individual responsibility attitudes and prosocial behavior. However, this would not have been expected based on previous research. Another indirect path between the same variables is modeled using a hypothesized moderated mediation effect. The institutional influence of higher education proves to be a significant mediator of the proposed relationship, moderated by the amount of educational content in the fields of ethics, social and environmental responsibility. At mid-and-high levels of exposure to relevant educational content, this indirect path significantly influences the developing young adults' pro-environmental behaviors. The study results are discussed from the viewpoint of peripheral regions with a history of dysfunctional social capital mechanisms.

Endurance-type disciplines (running, cycling, biathlon) define the cyclic structure of an athlete's movements, which, in addition to functional parameters, also includes an adequate fitness profile. Based on the detection, analysis and evaluation of these parameters, it is possible to define the fitness profile of the competitors as well as possible mutual differences. The current case study analyzes the fitness profile of competitors of three different disciplines (middle and long distances, cycling, ski biathlon) of top-level competitors, members of national teams. The study was conducted: Uroš Gutić (UG) - runner middle and long distances, member of AK "Sarajevo" and the BIH athletic national team; Milan Milivojević (MM) – cyclist, member of Cycling club "Borac" Čačak (Serbia), and the member Serbian national team; Stefan Lopatić (SL) – ski biathlete, member SK "Romanija" Pale, and BIH national team. A total of 12 parameters were measured to assess the fitness profile (repetitive strength, explosive strength, speed and agility). The results recorded considerable homogeneity of the sample with mutual differences. In addition to mutual differences, all study participants are characterized by an extremely good fitness profile, as shown by the measurement results.  Article visualizations:

Antibody-drug conjugates (ADCs) are novel, highly potent drugs composed of a small molecule of an anticancer drug (payload)attached to humanized antibody recognizing an epitope on the surface of cancer cells. ADCs are rapidly expanding in the oncology field. By 2022, >180 ADC-based clinical trials have been conducted [1]. Most of these clinical trials are in phases I or II [1]. Several ADCs have been approved and used for the treatment of various malignancies (e.g., brentuximab vedotin (BV) for the treatment of CD30+ lymphomas, trastuzumab emtansine (T-DM1) for advanced/metastatic/or early-stage high-risk HER2-positive breast cancer with residual disease after neoadjuvant treatment) [2]. Read more in the PDF.

Deaf children, due to the impossibility of transforming the internal speech scheme into expressive speech in mutual written communication, make maximum use of abbreviated speech schemes that determine the ability to communicate. The aim of the study is to examine the content comprehensibility and semantic level of written communication of deaf children through the number of used types oaf words in written communication, and to determine the existence of statistical significaance of differences between deaf and hearing respondents in the use of word types at the level of statistical significance p=0.001. The study was conducted on a sample of 140 respondents. The first subsample of respondents, the experimental group consisted of 70 deaf students, and the second subsample, a control group of 70 hearing students, of the same chronological age and gender. The measurement instrument “Test of understanding the written form of expression” was applied. The frequencies and percentages of responses to each of the variables used were calculated. The t-test and the F (Fisher) test were used to determine the statistical significance of the differences between deaf and hearing subjects. The results of the study showed that deaf students do not have contextual understanding and recognition of word types through testing of linguistic competence in relation to the hearing population, because 67.10% do not understand, and 10.00% of deaf students partially understand the contextual application of word types in writing textual task. Deaf students in the written form of communication use nouns (65.70%), pronouns (34.30%), verbs (45.7%), adjectives (28.60%), adverbs (22.90), prepositions (54.30%), exclamations (15.70%), particles (12.90%) and numbers (32.90%). There is a statistically significant difference between hearing and deaf respondents, in favor of hearing, in all applied types of words, except the use of verbs.

Background Gastrointestinal (GI) functions are controlled by the enteric nervous system (ENS) in vertebrates, but data on snakes are scarce, as most studies were done in mammals. However, the feeding of many snakes, including Crotalus atrox , is in strong contrast with mammals, as it consumes an immense, intact prey that is forwarded, stored, and processed by the GI tract. We performed immunohistochemistry in different regions of the GI tract to assess the neuronal density and to quantify cholinergic, nitrergic, and VIPergic enteric neurons. We recorded motility patterns and determined the role of different neurotransmitters in the control of motility. Neuroimaging experiments complemented motility findings. Results A well-developed ganglionated myenteric plexus (MP) was found in the oesophagus, stomach, and small and large intestines. In the submucous plexus (SMP) most neurons were scattered individually without forming ganglia. The lowest number of neurons was present in the SMP of the proximal colon, while the highest was in the MP of the oesophagus. The total number of neurons in the ENS was estimated to be approx. 1.5 million. In all regions of the SMP except for the oesophagus more nitric oxide synthase+ than choline-acetyltransferase (ChAT)+ neurons were counted, while in the MP ChAT+ neurons dominated. In the SMP most nerve cells were VIP+, contrary to the MP, where numerous VIP+ nerve fibers but hardly any VIP+ neuronal cell bodies were seen. Regular contractions were observed in muscle strips from the distal stomach, but not from the proximal stomach or the colon. We identified acetylcholine as the main excitatory and nitric oxide as the main inhibitory neurotransmitter. Furthermore, 5-HT and dopamine stimulated, while VIP and the ß-receptor-agonist isoproterenol inhibited motility. ATP had only a minor inhibitory effect. Nerve-evoked contractile responses were sodium-dependent, insensitive to tetrodotoxin (TTX), but sensitive to lidocaine, supported by neuroimaging experiments. Conclusions The structure of the ENS, and patterns of gastric and colonic contractile activity of Crotalus atrox are strikingly different from mammalian models. However, the main excitatory and inhibitory pathways appear to be conserved. Future studies have to explore how the observed differences are an adaptation to the particular feeding strategy of the snake.

The ammonium nitrate (AN) and fuel oil (FO) mixture known as ANFO is a typical representative of non-ideal explosives. In contrast to ideal explosives, the detonation behavior of ANFO exhibits a strong dependence on charge diameter, existence, and properties of confinement, with a large failure diameter and long distance required to establish steady-state detonation. In this study shock initiation and propagation of detonation in ANFO were studied experimentally by determining the detonation velocity at different distances from the initiation point, as well as by numerical modeling using AUTODYN hydrodynamics code and a Wood–Kirkwood detonation model incorporated into EXPLO5 thermochemical code. The run-to-steady-state detonation velocity distance was determined as a function of charge diameter, booster charge mass, and confinement. It was demonstrated that a Lee–Tarver ignition and growth reactive flow model with properly calibrated rate constants was capable of correctly ascertaining experimentally observed shock initiation behavior and propagation of detonation in ANFO, as well as the effects of charge diameter, booster mass, and confinement.

We provide a new efficient version of the backpropagation algorithm, specialized to the case where the weights of the neural network being trained are sparse. Our algorithm is general, as it applies to arbitrary (unstructured) sparsity and common layer types (e.g., convolutional or linear). We provide a fast vectorized implementation on commodity CPUs, and show that it can yield speedups in end-to-end runtime experiments, both in transfer learning using already-sparsified networks, and in training sparse networks from scratch. Thus, our results provide the first support for sparse training on commodity hardware.

In this paper we are interested to model quantum signal by statistical signal processing methods. The Gaussian distribution has been considered for the input quantum signal as Gaussian state have been proven to a type of important robust state and most of the important experiments of quantum information are done with Gaussian light. Along with that a joint noise model has been invoked, and followed by a received signal model has been formulated by using convolution of transmitted signal and joint quantum noise to realized theoretical achievable capacity of the single quantum link. In joint quantum noise model we consider the quantum Poisson noise with classical Gaussian noise. We compare the capacity of the quantum channel with respect to SNR to detect its overall tendency. In this paper we use the channel equation in terms of random variable to investigate the quantum signals and noise model statistically. These methods are proposed to develop Quantum statistical signal processing and the idea comes from the statistical signal processing.