Miracle-dz/UniFAR-FTUs huggingface dataset:
The determination of pure beta-emitters and their behaviour in a salt-marsh environment

The thesis describes the development of analytical procedures for the isolation and measurement of anthropogenic pure beta-emitting radioisotopes in low-level radioactive wastes and environmental samples. The research focussed on three key pure beta-emitting radioisotopes, namely 63Ni, 90Sr and 99Tc. Iron-55, which decays by electron capture, was also investigated. Source preparation and measurement techniques based on liquid scintillation counting were developed and optimised to permit the low-level measurement of all four radioisotopes. In particular, a technique was developed for increasing the amount of stable Fe that may be loaded into scintillant, reducing the limit of detection achievable for 55Fe measurement and increasing the sensitivity of analysis for 55Fe in Fe-rich materials such as sediments and steels. Chemistries for the isolation of the four radioisotopes were studied and optimised. Solvent extraction was chosen for the specificity offered by the technique. In most instances, improvements in separation efficiency were achieved by adsorbing the extractant onto an inert support producing an extraction chromatographic material. Key separation techniques were then combined to produce a sequential separation scheme that permitted a more rapid analysis of the four radioisotopes on a single sample. The sequential separation technique was then optimised for the analysis of 55Fe, 63Ni, 90Sr and 99Tc in both low-level wastes and environmental matrices (mainly sediments). Such separation schemes are crucial to the efficient analysis of samples in limited time spans and are vital when the amount of sample available is restricted. The optimised methods were used to investigate levels of anthropogenic pure beta-emitters in a saltmarsh sediment core collected from the Esk Estuary in Cumbria. Analysis of the four beta emitting radioisotopes was complemented by the analysis of major elements, trace elements and gamma emitting radioisotopes. This information was used to determine the behaviour of the beta emitters following deposition within the saltmarsh environment. Although all four beta emitters were detected in the core, only 90Sr and 99Tc were at sufficiently high levels to permit a more thorough investigation. The combination of geochemica! analysis and radiochemical analysis of this range of radioisotopes with widely varying chemistries has allowed a range of possible pre- and post-depositional processes to be investigated as well as providing data on the levels of previously unmeasured beta emitters in the saltmarsh environment. Such information is essential in assessing the long-term retention and potential re-release of these radioisotopes and their importance in radiological dose assessment. The information also has wider implications to the behaviour of inorganic pollutants in coastal waters.
La lectura en pantalla en las aulas

Un texto se comprende mejor si esta impreso en papel que si lo leemos en pantalla. El equipo de investigacion de la ERI-Lectura de la Universidad de Valencia, a traves de su Proyecto NeuroPapel, explora las causas que hay tras este fenomeno y busca soluciones que garanticen una eficiente introduccion de los dispositivos digitales en las aulas.
Empirical examination of the constraints and causes of crop seed losses in Ghana

ABSTRACT Development interventions have endeavored to improve the availability of improved seeds to enhance agricultural productivity in West African sub-region. The objective of this study, therefore, was to examine the constraints of seed production and the causes of seed losses in Northern Ghana. We used primary data on farm information collected from 62 seed producers in northern Ghana. The data were analyzed descriptively and supported by the Chi-square test. The results indicated that high input costs, undeveloped marketing system and seed distribution channels, erratic rainfall pattern, and inadequate machinery constituted the main challenges for seed producers. Field pests, poor storage structures, poor farm hygiene, and poor bagging process significantly influenced seed losses. We recommend, among other interventions, that agricultural policies of Ghana, such as “the planting for food and jobs” and the agricultural input subsidy program, provide seed producers with technical and financial support to reduce cost of production and seed losses. There is also the need to strengthen seed trade associations to provide technical backstopping and the needed services to actors in the seed value chain of Ghana.
Parametric Shape Estimation of Human Body Under Wide Clothing

The shape of the human body plays an important role in many applications, such as those involving personal healthcare and virtual clothing try-ons. However, accurate body shape measurements typically require the user to be wearing a minimal amount of clothing, which is not practical in many situations. To resolve this issue using deep learning techniques, we need a paired dataset of ground-truth naked human body shapes and their corresponding color images with clothes. As it is practically impossible to collect enough of this kind of data from real-world environments to train a deep neural network, in this paper, we present the Synthetic dataset of Human Avatars under wiDE gaRment (SHADER). The SHADER dataset consists of 300,000 paired ground-truth naked and dressed images of 1,500 synthetic humans with different body shapes, poses, garments, skin tones, and backgrounds. To take full advantage of SHADER, we propose a novel silhouette confidence measure and show that our silhouette confidence prediction network can help improve the performance of state-of-the-art shape estimation networks for human bodies under clothing. The experimental results demonstrate the effectiveness of the proposed approach. The code and dataset are available at https://github.com/YCL92/SHADER.
FDM: Fuzzy-Optimized Data Management Technique for Improving Big Data Analytics

Big data analytics and processing require complex architectures and sophisticated techniques for extracting useful information from the accumulated information. Visualizing the extracted data for real-time solutions is demanding in accordance with the semantics and the classification employed by the processing models. This article introduces fuzzy-optimized data management (FDM) technique for classifying and improving coalition of accumulated information based semantics and constraints. The dependency of the information is classified on the basis of the relationships modeled between the data based on the attributes. This technique segregates the considered attributes based on similarity index boundaries to process complex data in a controlled time. The performance of the proposed FDM is analyzed using a real-time weather forecast dataset consisting of sensor data (observed) and image data (captured). With this dataset, the functions of FDM such as input semantics analytics and classification based on similarity are performed. The metrics classification and processing time and similarity index are analyzed for the varying data sizes, classification instances, and dataset records. The proposed FDM is found to achieve 36.28% less processing time for varying classification instances, and 12.57% high similarity index.
Drug Susceptibility Testing and Synergistic Antibacterial Activity of Curcumin with Antibiotics against Enterotoxigenic Escherichia coli

Aim: This study investigated the susceptibility of Enterotoxigenic Escherichia coli to curcumin, as well as its synergistic effect with 12 antimicrobial drugs. Methods and Results: Our study shows that curcumin did not affect bacterial growth. The antimicrobial susceptibility of curcumin and antibiotic synergy were identified using disc diffusion on Mueller-Hinton agar. The strain of Enterotoxigenic Escherichia coli used was resistant to Ampicillin, Amoxicillin/Clavulanic acid, Ampicillin/Sulbactam, Ciprofloxacin, and Cefazolin. There was synergy between curcumin and the majority of antibiotics tested. Maximum synergy was observed with combinations of 330 µg/mL curcumin and Ceftazidime, followed by Cefotaxime, Amoxicillin/Clavulanic acid, Ampicillin, Aztreonam, Trimethoprim, Ciprofloxacin, Ceftriaxone, Cefazolin, Tetracycline, and Imipenem. Conclusion: Our findings indicated that curcumin might be useful as a combinatorial strategy to combat the antibiotic resistance of Enterotoxigenic Escherichia coli.
Codesign of Switching Sequence and Diode Parameters for Multiple Pattern Optimization in Time-Modulated Arrays

An innovative method for the design of time-modulated arrays (TMAs) is presented in this letter. The time-domain switching sequence and the parameters of the radio-frequency diodes implementing the time modulation are simultaneously synthesized in order to optimize the features of multiple harmonic beam patterns generated by the TMA. In the proposed codesign strategy, the analysis of the dynamic linear and nonlinear phenomena occurring during a time-based radiation is accurately carried out, including the mutual coupling effects between the radiating elements. A set of representative examples, drawn from an exhaustive numerical validation, is reported and discussed to assess the advantages and the effectiveness of the proposed codesign strategy.
Next-generation sequencing yields the mitochondrial genome of black-streaked Scimitar Babbler Pomatorhinus gravivox (Passeriformes: Timaliidae)

Abstract In this study, we first sequenced and described the complete mitochondrial genome of Pomatorhinus gravivox. The whole genome of P. gravivox was 16,963 bp in length and contained 13 protein-coding genes, 22 transfer RNA genes, two ribosome RNA genes, and one non-coding control region. The overall base composition of the mitochondrial DNA was 29.10% for A, 23.12% for T, 14.56% for G, and 33.22% for C, with a GC content of 47.78%. A phylogenetic tree confirmed that P. gravivox was sister to P. ruficollis and belonged to family Timaliidae. This information will be useful in the current understanding of the phylogeny and evolution of Timaliidae.
PPD: A Scalable and Efficient Parallel Primal-Dual Coordinate Descent Algorithm

Dual Coordinate Descent (DCD) is one of the most popular optimization methods. The parallelization of DCD is difficult, as DCD is sequential in nature. As such, simultaneously running multiple DCD threads on batches of data elements causes result inaccuracy and slow convergence, due to the concurrent updates of multiple coordinates. Some parallelization methods adopt separable approximate functions that depend on the degree of parallelism. Such dependencies result in both poor scalability and slow-convergence. To address these challenges, in this paper we present a new parallel primal-dual algorithm for DCD, called PPD. In PPD, the block data distribution is utilized to obtain a new approximate function that is independent from the parallelism. Moreover, PPD is designed with a novel primal-dual acceleration scheme to approach the optimal solution closely and quickly. We demonstrate the advantages of PPD in terms of scalability and efficiency through experiments.
Phenotypic and proteomic characterization of Dbr48, a candida albicans cell wall protein. (c2018)

The fungus Candida albicans is an opportunistic pathogen that possesses multiple factors and mechanisms that control virulence. The most important virulence factors and antigenic determinants are the cell wall proteins as they are the first elements contacting the host. Ddr48 is a cell wall protein consisting of 212 amino acids. This protein had been previously studied where it was found to be required for tolerance to hydrogen peroxide oxidative stress, drug resistance, filamentation and virulence. DDR48 was found to be haploid insufficient and essential as only a heterozygous strain was generated and not a homozygous null strain. In this study, we aimed to further elucidate the role of Ddr48 by subjecting our DDR48 heterozygous strain to a battery of assays in order to achieve additional phenotypic characterization. In addition, a proteomic approach was utilized to determine differentially expressed cell wall proteins detected exclusively in the wild type strain. Our results showed that the heterozygous mutant strain exhibited a 10% decrease in adhesion mirrored by a 20% decrease in biofilm formation, and a slight sensitivity to oxidative stress agents and SDS. Both strains showed similar hyphae formation ability, temperature tolerance, calcofluor white and Congo red sensitivities, chitin content, and virulence. For proteomic characterization, a total of 8 and 10 proteins were identified exclusively in the wild type strain grown under filamentous and non-filamentous conditions
Selecting music for exercise: The music preferences of UK exercisers

Abstract There is currently little information regarding the music preferences of exercisers. This absence might have important implications given suggestions that an aversion to a music style and/or piece might constrain the proposed extra-musical benefits. This study aimed to address this. Utilising an online survey, one thousand, one hundred and forty-five UK exercisers were questioned as to their listening preferences whilst exercising. Overall, the results highlighted a predilection for genres such as Pop, Dance/House and Rock. However, Chi-squared analysis revealed some gender differences; for example, the tendency for males to prefer “heavier” music styles. There was also a move towards less mainstream/contemporary music with age for both males and females. The outcomes of this study offer guidance to those tasked with selecting musical accompaniment for exercise within both the practical and research domains.
COMPARATIVE ANALYSIS OF MASS-BALANCED MODELS FOR A TROPICAL RESERVOIR TO ASSESS THE IMPACT OF A MANAGEMENT PRACTICE

Mass-balanced models were constructed for pre and post fishing ban period to compare the possible impacts of the introduction of the fishing closure management practice on the reservoir resources through estimating the trophic status and energy flow. The decreased biomass of high value fishes (Carps, Sheatfishes, Tilapia, Anabas testudineus and Knife fishes) despite the implementation of fishing closure pinpoints the excessive fishing pressure on these target species. However, the increased biomass of minnows and clupeids may pose a serious threat to reservoir stability and sustainability altering the trophic structure. Mixed trophic impact indicates that functional groups at the lower trophic level had positive effect on most of the groups. The ratio of primary production/respiration was 13.154 and 1.969 during pre-ban and post-ban season respectively. The relative overhead increased (22.48%) while ascendancy was found to be lower (-0.43%) during the post-ban period. The reservoir was more vulnerable to disruptions before the implementation of fishing ban. Some ecosystem attributes such as TPP/TB, TB/TST, TPP/TR, TPP – TR reveal that the Kaptai reservoir gained higher resilience, stability, maturity and complexity in food web after discontinuing the unlimited fishing access but it still requires attention to overcome the stresses associated with human activities.
The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks

Learning in neuronal networks has developed in many directions, in particular to reproduce cognitive tasks like image recognition and speech processing. Implementations have been inspired by stereotypical neuronal responses like tuning curves in the visual system, where, for example, ON/OFF cells fire or not depending on the contrast in their receptive fields. Classical models of neuronal networks therefore map a set of input signals to a set of activity levels in the output of the network. Each category of inputs is thereby predominantly characterized by its mean. In the case of time series, fluctuations around this mean constitute noise in this view. For this paradigm, the high variability exhibited by the cortical activity may thus imply limitations or constraints, which have been discussed for many years. For example, the need for averaging neuronal activity over long periods or large groups of cells to assess a robust mean and to diminish the effect of noise correlations. To reconcile robust computations with variable neuronal activity, we here propose a conceptual change of perspective by employing variability of activity as the basis for stimulus-related information to be learned by neurons, rather than merely being the noise that corrupts the mean signal. In this new paradigm both afferent and recurrent weights in a network are tuned to shape the input-output mapping for covariances, the second-order statistics of the fluctuating activity. When including time lags, covariance patterns define a natural metric for time series that capture their propagating nature. We develop the theory for classification of time series based on their spatio-temporal covariances, which reflect dynamical properties. We demonstrate that recurrent connectivity is able to transform information contained in the temporal structure of the signal into spatial covariances. Finally, we use the MNIST database to show how the covariance perceptron can capture specific second-order statistical patterns generated by moving digits. Author summary The dynamics in cortex is characterized by highly fluctuating activity: Even under the very same experimental conditions the activity typically does not reproduce on the level of individual spikes. Given this variability, how then does the brain realize its quasi-deterministic function? One obvious solution is to compute averages over many cells, assuming that the mean activity, or rate, is actually the decisive signal. Variability across trials of an experiment is thus considered noise. We here explore the opposite view: Can fluctuations be used to actually represent information? And if yes, is there a benefit over a representation using the mean rate? We find that a fluctuation-based scheme is not only powerful in distinguishing signals into several classes, but also that networks can efficiently be trained in the new paradigm. Moreover, we argue why such a scheme of representation is more consistent with known forms of synaptic plasticity than rate-based network dynamics.
Forensic Analysis of Hair by Scanning Electron Microscopy in Domesticated and Wild animals

India is referred to be one of the richest biodiverse nations of the world. India is home for 400 mammalian species out of which 129 species are protected under Wildlife (Protection) Act 1972 (India) and a arbitrator to Convention on International Trade in endangered species. Illegal trade of the wildlife products are key risk to conservation and responsible for regional obliteration to these species. In forensic identification, the studies on animal hair has remain limited to cross-species comparisons from the same family, but there is also considerable changes within family like scale layer differentiation growth pattern, tensile strength, granulation and elemental profile of the animal hair. The early pioneering work on species characterizations from hair includes studies by McCurtie (1886), Hausman (1920, 1944), and Hardy and Plitt (1940). Descriptive guides on microscopic hair characteristics for some important mammalian species of particular regions have been contributed by Brunner and Coman (1974), Moore et al., International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 8 Number 02 (2019) Journal homepage: http://www.ijcmas. com
Discourse roles of particles in Chiyao

This paper seeks to investigate the discourse roles of particles in Chiyao along with their environment of occurrence and scope of interpretation. The analysis is based on the data collected through audio-recording of traditional narratives which were later transcribed to identify utterances with particles. The findings establish that particles in Chiyao can occur in three major types of environment, namely within the NP as is the case with question particles, demonstrative particles, and exclusive particles; within the VP as is the case with question particles, exclusive particles, and negative particles; and in pre-sentential position (e.g. emotional particles, additive particles, and contrast particles). Concerning the scope of interpretation, the analysis indicates that particles that occur within the NP have their scope of interpretation limited to the NP, while the meaning of those that occur as part of the VP or in pre-sentential position applies to the entire sentence. As for the discourse roles, it is shown that the Chiyao particle performs different significant roles, including emphatic roles, such as emphasising negation; and exclusive roles, that is, to single out some elements within a clause from the rest of a clause in order to show focus. It is further shown that particles are used to signal interrogation, and for anaphoric purposes. Moreover, particles in Chiyao indicate deixis as well as emotions such as surprise, anger, disapproval and shock. Finally, particles indicate contrast and addition. These findings lead to a conclusion that the use of particles represents an important communicative strategy in Chiyao.
Applications of Deep Learning and Fuzzy Systems to Detect Cancer Mortality in Next-Generation Genomic Data

In the era of advanced precision medicine, next-generation genomic data are crucial to achieve breakthroughs in cancer medicine. Effective cancer mortality risk estimation for genomic data associated with cancer remains a vital challenge. The combination of machine learning algorithms and conventional survival analysis can advance the detection of high-risk missense mutation variants and candidate genes associated with cancer mortality in next-generation genomic data. In this article, a fuzzy logic system combined with machine learning algorithms and conventional survival analysis named FuzzyDeepCoxPH was proposed to identify high-risk missense mutation variants and candidate genes highly associated with cancer mortality. DL-derived abstracted weights and Cox proportional hazards (CoxPH) ratios were used to develop four model-based risk scores to consider the factor importance associated with risk stratification, time-varying effects, and individual and interaction effects among features. Fuzzy rules based on a fuzzy logic system were designed to integrate these considerations by merging four model-based risk scores to develop advanced risk estimation. The clinical features and next-generation sequencing of deoxyribonucleic acid and ribonucleic acid genomic data were used to evaluate FuzzyDeepCoxPH performance. The results indicated that FuzzyDeepCoxPH can effectively distinguish high-risk variants and candidate genes related to cancer mortality. In FuzzyDeepCoxPH, the fuzzy logic system was applied to combine DL-based and CoxPH-based models to provide a comprehensive cancer mortality risk estimation for cancer medicine.