Aristotle’s Refutation of the Eleatic Argument in Physics I.8

In this paper, I show that Aristotle’s refutation of the Eleatic argument in Physics I.8 is based on the idea that a thing at the starting point of coming to be is composite and is made up of what underlies and a priva­tion. In doing so, I clarify how the concept of accidentality as used in his solution should be understood in relation to the composite nature of what comes to be. I also suggest an explanation of why Aristotle’s discus­sion of the Eleatic dilemma in Physics I.8, unlike his discussion in the previous chapter, is not clear.

The Conceptualisation of Risk and Protective Factors in Child Sex Offenders: A Preliminary Theoretical Model

<p>The current preoccupation of criminal justice practitioners and policy makers with the prediction of criminal risk has resulted in a conceptualisation of risk as clusters of phenomena that correlate with recidivism. A reliance on these phenomena as explanations for the causes of sexual offending is a mistake. The growing gap between theory and practice highlights the need for theoretical models that can account for the existence and influence of risk-related factors. Two key issues that need to be addressed concern the composite nature of dynamic risk factors, and questions over their ontological status, that is, whether or not they exist outside of prediction contexts. This thesis begins with an exploration and reconceptualisation of the phenomena that increase and decrease risk of sexual offending; the focus is then widened to include human agency, motivation, and values. These normative features are integrated with risk-related factors within the action-based Agency Model of Risk (AMR). This dynamic, interactional model highlights the importance of the relationship between the agent and their context, with both contributing to the patterns of behaviour that lead to an offence. Finally the AMR is applied to a number of risk domains for sexual offenders, and its utility in explaining their behaviour is discussed. The aim of this thesis is to encourage a broader focus on human values, agency, and contexts that influence an individual’s goal-directed behaviour.</p>

The Conceptualisation of Risk and Protective Factors in Child Sex Offenders: A Preliminary Theoretical Model

<p>The current preoccupation of criminal justice practitioners and policy makers with the prediction of criminal risk has resulted in a conceptualisation of risk as clusters of phenomena that correlate with recidivism. A reliance on these phenomena as explanations for the causes of sexual offending is a mistake. The growing gap between theory and practice highlights the need for theoretical models that can account for the existence and influence of risk-related factors. Two key issues that need to be addressed concern the composite nature of dynamic risk factors, and questions over their ontological status, that is, whether or not they exist outside of prediction contexts. This thesis begins with an exploration and reconceptualisation of the phenomena that increase and decrease risk of sexual offending; the focus is then widened to include human agency, motivation, and values. These normative features are integrated with risk-related factors within the action-based Agency Model of Risk (AMR). This dynamic, interactional model highlights the importance of the relationship between the agent and their context, with both contributing to the patterns of behaviour that lead to an offence. Finally the AMR is applied to a number of risk domains for sexual offenders, and its utility in explaining their behaviour is discussed. The aim of this thesis is to encourage a broader focus on human values, agency, and contexts that influence an individual’s goal-directed behaviour.</p>

First-principles modeling of the infrared spectrum of antigorite

The infrared absorption spectrum of a natural antigorite sample from New Caledonia is compared to its theoretical counterpart computed for the pristine antigorite m=17 polysome within the density functional perturbation theory framework. The theoretical model reproduces most of the bands related to Si-O stretching in the 800–1300 cm−1 range, OH libration, hindered OH translation and SiO4 bending in the 400–800 cm−1 range, and OH stretching in the 3500–3700 cm−1 range. Most of the observed bands have a composite nature involving several vibrational modes contributing to their intensity, except the apical and one of the basal Si-O stretching bands whose intensity is carried by a single mode. The peculiarity of the antigorite structure favors a localization of the Si-O and OH stretching modes in specific regions of the unit cell. Weaker Si-O stretching bands experimentally observed at 1205 and 1130 cm−1 are related to the occurrence of 6- and 8-reversals in the antigorite structure, respectively. The distribution of OH bond lengths leads to an asymmetric distribution of frequencies consistent with the width and the shape of the experimentally observed OH stretching band. It also leads to a strong distribution of OH libration frequencies ranging from 600 to 830 cm−1 explaining the asymmetry of the band observed at 648 cm−1 in the antigorite spectrum.

A Dynamic Factor and Neural Networks Analysis of the Co-movement of Public Revenues in the EMU

This paper shows that the co-movement of public revenues in the European Monetary Union (EMU) is driven by an unobserved common factor. Our empirical analysis uses yearly data covering the period 1970–2014 for 12 selected EMU member countries. We have found that this common component has a significant impact on public revenues in the majority of the countries. We highlight this common pattern in a dynamic factor model (DFM). Since this factor is unobservable, it is difficult to agree on what it represents. We argue that the latent factor that emerges from the two different empirical approaches used might have a composite nature, being the result of both the more general convergence of the economic cycles of the countries in the area and the increasingly better tuned tax structure. However, the original aspect of our paper is the use of a back-propagation neural networks (BPNN)-DF model to test the results of the time-series. At the level of computer programming, the results obtained represent the first empirical demonstration of the latent factor’s presence.

What Solar–Terrestrial Link Researchers Should Know about Interplanetary Drivers

One of the most promising methods of research in solar–terrestrial physics is the comparison of the responses of the magnetosphere–ionosphere–atmosphere system to various types of interplanetary disturbances (so-called “interplanetary drivers”). Numerous studies have shown that different types of drivers result in different reactions of the system for identical variations in the interplanetary magnetic field. In particular, the sheaths—compression regions before fast interplanetary CMEs (ICMEs)—have higher efficiency in terms of the generation of magnetic storms than ICMEs. The growing popularity of this method of research is accompanied by the growth of incorrect methodological approaches in such studies. These errors can be divided into four main classes: (i) using incorrect data with the identification of driver types published in other studies; (ii) using incorrect methods to identify the types of drivers and, as a result, misclassify the causes of magnetospheric-ionospheric disturbances; (iii) ignoring a frequent case with a complex, composite, nature of the driver (the presence of a sequence of several simple drivers) and matching the system response with only one of the drivers; for example, a magnetic storm is often generated by a sheath in front of ICME, although the authors consider these events to be a so-called “CME-induced” storm, rather than a “sheath-induced” storm; (iv) ignoring the compression regions before the fast CME in the case when there is no interplanetary shock (IS) in front of the compression region (“sheath without IS” or the so-called “lost driver”), although this type of driver generates about 10% of moderate and large magnetic storms. Possible ways of solving this problem are discussed.

Bacterial Communities in Concrete Reflect Its Composite Nature and Change with Weathering

ABSTRACT Concrete is an extreme but common environment and is home to microbial communities adapted to alkaline, saline, and oligotrophic conditions. Microbes inside the concrete that makes up buildings or roads have received little attention despite their ubiquity and capacity to interact with the concrete. Because concrete is a composite of materials which have their own microbial communities, we hypothesized that the microbial communities of concrete reflect those of the concrete components and that these communities change as the concrete ages. Here, we used a 16S amplicon study to show how microbial communities change over 2 years of outdoor weathering in two sets of concrete cylinders, one prone to the concrete-degrading alkali-silica reaction (ASR) and the other having the risk of the ASR mitigated. After identifying and removing taxa that were likely laboratory or reagent contaminants, we found that precursor materials, particularly the large aggregate (gravel), were the probable source of ∼50 to 60% of the bacteria observed in the first cylinders from each series. Overall, community diversity decreased over 2 years, with temporarily increased diversity in warmer summer months. We found that most of the concrete microbiome was composed of Proteobacteria, Firmicutes, and Actinobacteria, although community composition changed seasonally and over multiyear time scales and was likely influenced by environmental deposition. Although the community composition between the two series was not significantly different overall, several taxa, including Arcobacter, Modestobacter, Salinicoccus, Rheinheimera, Lawsonella, and Bryobacter, appear to be associated with ASR. IMPORTANCE Concrete is the most-used building material in the world and a biologically extreme environment, with a microbiome composed of bacteria that likely come from concrete precursor materials, aerosols, and environmental deposition. These microbes, though seeded from a variety of materials, are all subject to desiccation, heating, starvation, high salinity, and very high pH. Microbes that survive and even thrive under these conditions can potentially either degrade concrete or contribute to its repair. Thus, understanding which microbes survive in concrete, under what conditions, and for how long has potential implications for biorepair of concrete. Further, methodological pipelines for analyzing concrete microbial communities can be applied to concrete from a variety of structures or with different types of damage to identify bioindicator species that can be used for structural health monitoring and service life prediction.

Varietal identification and fingerprinting of Pearl Millet (Pennisetum glaucum L.) varieties and hybrid using morphological descriptors and SSR markers

Pearl Millet (Pennisetum glaucum) is the sixth most important cereal crop in the world. The genomic resources available in Pearl millet can be utilized for fingerprinting and screening of hybrids using SSR markers and will be helpful for the assessment of seed purity. Hence, the present study was focused on fingerprint popular pearl millet varieties and hybrids of Tamil Nadu for varietal identification and hybrid purity test. The varieties used for DNA fingerprinting were CO (Cu) 9, CO 10, Pearl Millet hybrid CO 9 along with the parents, A' line ICMA 93111A and R' line PT 6029-30. The morphological features were recorded to screen the cultivars. The Pearl millet hybrid CO 9 scored the highest value for more than four quantitative characters via., Number of productive tillers (4-6), Leaf blade length (60-68cm), Leaf blade width (4.0-4.5cm), number of nodes (8-10), and 1000 seed weight (13-14g) which is at par and comparable with the composite CO 10 and higher than that of the variety CO (Cu) 9. PCR was performed using 36 SSR primers to find out polymorphism among the varieties. The SSR markers ICMP3021 and PSMP2089 were able to selectively identify CO (Cu) 9 from the other varieties. Whereas, the SSR markers ICMP3018, PSMP2219, and PSMP2220 were used to distinguish CO 10 from the other varieties. Further, the CO10 variety produced additional alleles for all the markers due to its composite nature. Among the thirty-six SSR primers screened, none of them were found suitable to distinguish the TNAU hybrid CO 9 from its parents. The unique DNA fingerprints developed in the present study can be utilized for seed purity testing and varietal identification.

Prediction of COVID-19 Wide Spread in India using Time Series Forecasting Techniques

The assets of some of the enormous wealth are strain out due to the massive infectivity of COVID-19. India is a portion of the global wide spread of COVID- 19 engender by dreadful drastic respiratory syndrome corona virus 2. As of 15th July 2020, the Ministry of Health and Family Welfare has committed a total of 968857 instances, 612768 healings and 24914 demises in the country. Due to the heighten magnitude of number of instances, a professional working in the health departments, some forecasting methods would be required to forecast the number of instances in subsequent days. Due to a towering of uncertainty and lack of crucial information, quality models have shown stubby accuracy for long-term forecast. Among several machine learning models investigated, Time Series Forecasting like Facebook’s Prophet showed promising results. In this paper, we have predicted the number of committed, healed, demise instances of COVID-19 in India 60 days’ forwards, forecasted the Number of Committed instances, healed instances and demise instances of COVID-19 in India 30 days onwards. Relied on the consequences announced here, and due to the mostly composite nature of the COVID-19 eruption and variation in its deportment, this study suggests machine learning as an efficacious contrivance prototype to the eruption.