J, Volume 5, Issue 2 (June 2022) – 7 articles

Principal component analysis (PCA) is used to reduce the dimensionalities of high-dimensional datasets in a variety of research areas. For example, biological macromolecules, such as proteins, exhibit many degrees of freedom, allowing them to adopt intricate structures and exhibit complex functions by undergoing large conformational changes. Therefore, molecular simulations of and experiments on proteins generate a large number of structure variations in high-dimensional space. PCA and many PCA-related methods have been developed to extract key features from such structural data, and these approaches have been widely applied for over 30 years to elucidate macromolecular dynamics. This review mainly focuses on the methodological aspects of PCA and related methods and their applications for investigating protein dynamics. Full article

This work presents the application of radioactive tracers to detect and correct feed flowrate imbalances in parallel rougher flotation banks. Several surveys were conducted at Minera Los Pelambres concentrator, in banks consisting of 250 m³ mechanical flotation cells. The feed pulp distribution was estimated from the mean residence times, which were obtained from residence time distribution measurements. The tracer was injected in the feed distributor and the inlet and outlet tracer signals of cells 1 and 2 were measured by on-stream sensors. The baseline condition for the pulp distribution was defined by the valve settings in the feed distributor, which led to an unbalanced condition for two parallel rougher banks, with 34% of the pulp being fed to bank A and 66% to bank B. New valve configurations were evaluated, with a fraction of the feed being directed to the rougher bank C, which was not initially fed from the same distributor. The feed distribution was finally balanced with 49% of the pulp being fed to bank A versus 51% to bank B. Thus, the radioactive traces proved to be a powerful tool to industrially detect and improve feed distributions in parallel flotation circuits. Full article

Plasmonic photocatalytic nanocomposites of TiO₂ and Au nanoparticles (NPs) have recently attracted the attention of researchers, who aim to improve the photocatalytic activity of potential TiO₂ NPs. In this study, we report photocatalytic activity enhancement for a Au/TiO₂ nanocomposite prepared by the plasma–liquid interaction method using an atmospheric microplasma apparatus. The enhanced photocatalytic activity of the prepared Au/TiO₂ is demonstrated by the degradation of methylene blue (MB) in water under both ultraviolet (UV) and visible light irradiation. The prior treatment of TiO₂ with microplasma in a NH₃ and H₂O₂ solution is found to strongly improve the photocatalytic activity of both the treated TiO₂ NPs, as well as the synthesized Au/TiO₂ nanocomposite. Full article

The use of SF₆ in electrical insulation and fast-switching applications cannot be overemphasized. This is due to its excellent dielectric properties and high breakdown voltage, which are especially important for practical applications such as gas-insulated switchgears and pulsed power switches where pressurized SF₆ is used. Breakdown in the gas occurs via streamer–leader transition; however, this transition is difficult to quantify numerically at atmospheric pressure because of the electronegativity of the gas. In the present work, streamer discharges in SF₆ gas at pressures of 10 and 100 kPa were studied using a plasma fluid model implementation. Analysis of the electric field in the streamer body, streamer velocity, diameter, and the effect of the high electronegativity of the gas on streamer parameters are presented for positive polarity in a point-to-plane geometry. The streamers in SF₆ for non-uniform background fields are compared to those in air, which have already been studied extensively in the literature. Full article

Quantum matter (novel phases of matter at zero temperature with exotic properties) is a growing field with applications in its own domain, and in providing foundational support to quantum sciences fields more generally. The ability to characterize and manipulate matter at the smallest scales continues to advance in fundamental ways. This review provides a plain-language, non-technical description of contemporary activity in quantum matter for a general science audience, and an example of these methods applied to quantum neuroscience. Quantum matter is the study of topologically governed phases of matter at absolute zero temperature that exhibit new kinds of emergent order and exotic properties related to topology and symmetry, entanglement, and electronic charge and magnetism, which may be orchestrated to create new classes of materials and computational devices (including in the areas of spintronics, valleytronics, and quantum computing). The paper is organized to discuss recent developments in quantum matter on the topics of short-range topologically protected materials (namely, topological semimetals), long-range entangled materials (quantum spin liquids and fractional quantum Hall states), and codes for characterizing and controlling quantum systems. A key finding is that a shift in the conceptualization of the field of quantum matter may be underway to expand the core focus on short-range topologically protected materials to also include geometry-based approaches and long-range entanglement as additionally important tools for the understanding, characterization, and manipulation of topological materials. Full article

A statistical analysis of important DNA sequences and related proteins has been performed to study the relationships between monomers, and some general considerations about these macromolecules can be provided from the results. First, the most important relationship between sites in all the DNA sequences examined is that between two consecutive base pairs. This is an indication of an energetic stabilization due to the stacking interaction of these couples of base pairs. Secondly, the difference between human chromosome sequences and their coding parts is relevant both in the relationships between sites and in some specific compositional rules, such as the second Chargaff rule. Third, the evidence of the relationship in two successive triplets of DNA coding sequences generates a relationship between two successive amino acids in the proteins. This is obviously impossible if all the relationships between the sites are statistical evidence and do not involve causes; therefore, in this article, due to stacking interactions and this relationship in coding sequences, we will divide the concept of the relationship between sites into two concepts: affinity and correlation, the first with physical causes and the second without. Finally, from the statistical analyses carried out, it will emerge that the human genome is uniform, with the only significant exception being the Y chromosome. Full article

One of the weaknesses of current line-source models for predicting downwind concentrations from mobile sources is accounting for the dispersion of effluents. Most of the investigators in the field have taken different approaches over the last 50 years, ranging from the use of Pasquill–Gifford (P-G) dispersion curves to the use of equations based on atmospheric turbulence for point source dispersion. Madiraju and Kumar (2021) proposed a three-phase turbulence (TPT) model using the key features of mobile source dispersion that appear in the existing literature. This paper examines the performance of line-source models using an updated TPT model. The generic dispersion equations were considered from the SLINE 1.1, CALINE 4, ADMS, and SLSM models. Multiple air quality field data sets collected by other investigators near the roadways were used during this study. These include field data collected from the Idaho Falls Tracer Experiment 2008 (used as the dataset to compare with the initial model), the CALTRANS Highway 99 Tracer experiment, and the Raleigh 2006 experiment. The predicted concentrations were grouped under unstable and stable atmospheric conditions. The evaluation of the model was performed using several statistical parameters such as FB, NMSE, R², MG, VG, MSLE, and MAPE. The results indicate that the ADMS and SLINE 1.1 models perform better than CALINE4 and SLSM. SLINE 1.1 tends to overpredict for stable atmospheric conditions and underpredict for unstable atmospheric conditions. A trial test was performed to implement the TPT model in the basic line-source model (SLSM). The results indicate that the majority (FB, NMSE, R², and MSLE) of the indicators have improved and are in the satisfactory range of a good model performance level. Full article