Interaction Of Multiple Elements Research Articles

Engineering multi-ion doping by entropy for high energy storage density with high efficiency in amorphous thin film

In the field of stored energy materials, lead-free amorphous thin films have the advantages of high breakdown strength, excellent stability, environmental protection and pollution-free, and are a very competitive energy storage material. However, the difficulty of simultaneous optimization of polarization and breakdown strength has always been a difficulty in improving the energy storage properties of amorphous thin films. Entropy can be used to design multi-ion doping to improve the energy storage performance of amorphous film. Amorphous films with different entropy are prepared by sol-gel method doped with 50 % Zr4+ and 5 %, 10 %, 15 %, 20 % Bi(Mn0·5Ti0.5)O3. The Bi0·1Ba0·85Sr0·05Mn0·05Ti0·45Zr0·5O3 amorphous thin film prepared at medium entropy (S = 1.37) has a recoverable energy storage density of 107.4 J cm−3 at 8.42 MV cm−1, and the energy storage efficiency is 93.9 %. Under the interaction of multiple elements, entropy design can give full play to the advantages of composite effects, improve breakdown field strength and energy storage efficiency, and is a new method to enhance energy storage performance.

Mutational landscape in squamous cell carcinoma of the nail unit.

Squamous cell carcinoma (SCC) is the most common malignancy of the nail unit. Pathogenetic mechanisms are yet to be determined, and a deeper molecular characterization of this disease is still necessary. The aim was to obtain a molecular characterization of NU SCC samples using an NGS approach to identify the genetic drivers involved in this tumor. The presence of HPV infection was also assessed. Furthermore, the mutational status was correlated with specific clinical-pathological features for a better insight into the carcinogenesis of this uncommon tumor. We analysed twenty paraffin-embedded nail unit SCC samples from patients diagnosed with primary SCC of the nail unit by next genome sequencing. In the 20 tested samples, the neoplastic cells enrichment ranged from 10% to 50% (mean value: 25.7%). In 14/20 cases (70.0%), at least one mutation was detected; whereas in the other six cases (30.0%), no alterations were observed ('wild-type/WT cases'). Overall, a total of 23 mutations were identified in the 20 specimens. TP53 was the most mutated gene (6/20 cases, 30.0%), while cKit, GNAS, EGFR, DICER1 and CTNNB1 were observed in one sample each (5.0%). No clinical-pathological parameters (age, sex, depth of invasion-DOI, histological subtype, grading and HPV) were significantly associated with the mutational status. The nail unit SCC mutational landscape appeared to be heterogeneous, favouring the hypothesis of a complex pathogenesis and an interaction of multiple elements, including HPV infections. This wealth of information undoubtedly improves our understanding of SCC biology.

Materials representation and transfer learning for multi-property prediction

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences.

Single and mixed effects of metallic elements in maternal serum during pregnancy on risk for fetal neural tube defects: A Bayesian kernel regression approach

Studies of the association between prenatal exposure to metal elements and risk for neural tube defects (NTDs) have produced inconsistent results. Little research has examined the joint effects and interactions of multiple elements. This study examined 273 women with NTD-affected pregnancies and 477 controls. Cadmium, cobalt, chromium, copper, iron, mercury, manganese, molybdenum, lead, and zinc were quantified in maternal serum. Single and mixed effects of these elements on NTD risk were evaluated with Bayesian kernel machine regression, and the effects of individual elements were validated using logistic regression. As a result, NTD risk increased with the concentration of the mixture of the 10 elements. NTD risk rose as the levels of the five toxic elements increased, with effect sizes larger than the overall analyses, but they decreased, albeit non-significantly, as the levels of the five essential elements increased. Lead and manganese showed risk effects on NTDs, with odds ratios (ORs) of 1.94 (1.76–2.13) and 1.25 (1.14–1.38), respectively, with the remaining nine elements remaining at their median. Molybdenum showed a protective effect against NTDs with an OR 0.87 (0.90–0.94). The single-element results were validated using logistic regression. In conclusion, NTD risk increased with concentrations of the five toxic elements, with lead and manganese being the major contributors. Essential elements showed protective effects against NTD risk.

Alternating regimes of motion in a model with cell-cell interactions.

Cellular movement is a complex dynamic process, resulting from the interaction of multiple elements at the intra- and extracellular levels. This epiphenomenon presents a variety of behaviors, which can include normal and anomalous diffusion or collective migration. In some cases, cells can get neighborhood information through chemical or mechanical cues. A unified understanding about how such information can influence the dynamics of cell movement is still lacking. In order to improve our comprehension of cell migration we have considered a cellular Potts model where cells move actively in the direction of a driving field. The intensity of this driving field is constant, while its orientation can evolve according to two alternative dynamics based on the Ornstein-Uhlenbeck process. In one case, the next orientation of the driving field depends on the previous direction of the field. In the other case, the direction update considers the mean orientation performed by the cell in previous steps. Thus, the latter update rule mimics the ability of cells to perceive the environment, avoiding obstacles and thus increasing the cellular displacement. Different cell densities are considered to reveal the effect of cell-cell interactions. Our results indicate that both dynamics introduce temporal and spatial correlations in cell velocity in a friction-coefficient and cell-density-dependent manner. Furthermore, we observe alternating regimes in the mean-square displacement, with normal and anomalous diffusion. The crossovers between diffusive and directed motion regimes are strongly affected by both the driving field dynamics and cell-cell interactions. In this sense, when cell polarization update grants information about the previous cellular displacement, the duration of the diffusive regime decreases, particularly in high-density cultures.

Cognitive load in algebra: element interactivity in solving equations

Central to equation solving is the maintenance of equivalence on both sides of the equation. However, when the process involves an interaction of multiple elements, solving an equation can impose a high cognitive load. The balance method requires operations on both sides of the equation, whereas the inverse method involves operations on one side only. In an experiment, middle school students (N = 71) were randomly assigned to the balance and inverse methods to complete a pre-test, an acquisition phase and a post-test. Pre-test and post-test comparisons found that the inverse group outperformed the balance group for those equations that involved high element interactivity. Instructional efficiency measures further confirmed that the balance method imposed higher cognitive load. The inverse method was capable of reducing cognitive load due to the interacting elements.

金沙江干热河谷植物叶片元素含量在地表凋落物周转中的作用

叶片的化学计量学特征在植物响应环境变化,决定植物的生后效应中具有重要的偶联作用。为了阐明植物叶片生源要素含量对凋落物周转的影响,分析了金沙江干热河谷萨瓦纳草地生态系统植物叶片的化学计量学特征与凋落物周转时间的关系。结果显示:凋落物周转受到多重生源要素及其交互作用的影响,其中K与凋落物周转时间存在显著的正相关关系,而S、Mn、Mg元素具有负关系,表明K可能抑制凋落物的分解,而S、Mn、Mg元素可能会促进凋落物分解。在物种水平上K、S、Mn分别与凋落物周转时间存在显著的相关性, K、S组合解释了16.93%的凋落物周转时间变异;样方水平上,K、S、Mn、Mg分别与凋落物周转时间具有显著相关性,虽然N对凋落物的周转时间影响不显著,但当N与K及其交互作用对凋落物周转时间解释了37.42%的变异。其它元素组合也可在不同程度上解释了凋落物周转时间的变异。多元要素的互作效应表明元素间可能存在拮抗和协同效应,凋落物分解过程中可能受到多重分解者的共同作用,而不同分解者会受到不同的元素限制。未来的研究应当注重N、P以外的元素在生物地球化学循环中的作用。;Leaf elemental stoichiometry has an important role in linking plant response to environmental changes and its afterlife effect. Plant leaf stoichiometry can change dramatically across environment gradients such as grazing, moisture and soil fertilities and consequently affect ecosystem function (e.g. nutrient cycle) in turn. Although concentrations of elements in leaves have already be known to have their effects on litter decomposability, the effects of multiple elements were less studied, especially other elements besides C, N, P. Since there are coordinated decomposition rates of different organs, it can be hypothesized that leaf stoichiometry could capture the variation of litter turnover time and therefore feedback to ecosystem function. To explore the effects of biogenic element concentration in leaves on litter turnover, biomass and litter aboveground were collected from 38 plots in a savanna grassland (E101 °35 '-102 °06 ', N25 °23 '-26 °06 ') which situated at the arid-hot valley of Jinsha river. Biomass and litter were classified into species before weighting. Litter turnover time was calculated as the fresh biomass : litter pool ratio. The most important biogenic element (i.e. N, P, K, Ca, Mg, S, Mn, Cu, Fe and Zn) concentrations in leaves were measured. The relationships between these element concentrations in leaves and litter turnover time were analyzed. The results showed that litter turnover may be affected by multiple elemental concentrations in leaves. K has a significantly positive relationship with litter turnover time while S, Mn and Mg were negatively related to litter turnover time which indicated K might inhibit litter decomposition while S, Mn and Mg might promote it. Contrary to previous studies, neither N nor P was significantly related to litter turnover time. K, S and Mn were related to litter turnover time significantly at species level respectively. K and S jointly explained 16.93% variation at species level. K, S, Mg and Mn were related to litter turnover time significantly at plot level respectively. Although N had no significant effect on litter turnover time alone, K-N and their interaction explained 37.42% variation at plot level. This combination explained 18.37% more variation than K-N additive effect. It was followed by K-Mn combination which explained 36.64% variation at plot level. Other elemental combinations could also significantly explained variation of litter turnover time to some extent. K was positively related to litter turnover time although it was positively related to Mn and Mg which were in turn negatively related to litter turnover time. The results here suggest that K might be the only element that inhibits litter decomposability in this dry and hot area. The mismatch of elemental effects on litter turnover time between at species level and at plot level might result from element transfer in litters or their complementary effects in decomposer's growth. The interactions of multiple elements indicated that there might be synergic and antagonistic effects between elements and suggested that litter turnover time would be affected by multiple decomposers which may be limited by different elements. The results here suggest that future studies should make attentions on other elements besides N and P to explore their roles in biogeochemistry processes.

Prediction of flow-generated noise produced by acoustic and aerodynamic interactions of multiple in-duct elements

Flow-generated noise problem caused by in-duct elements is due to the complicated acoustic and turbulent interactions of multiple in-duct flow noise sources. The approach of partially coherent sound fields used previously by Mak and Yang [C.M. Mak, J. Yang, Flow-generated noise radiated by the interaction of two strip spoilers in a low speed flow ducts, Acta Acust united with Acustica 88 (2002) 861–868] and Mak [C.M. Mak, A prediction method for aerodynamic sound produced by multiple elements in air ducts, J Sound Vib 287 (2005) 395–403] is adopted to formulate the sound powers produced by interactions of multiple elements at frequencies below and above the cut-on frequency of the lowest transverse duct mode. The study indicates that the level and spectral distribution of the additional acoustic energy produced by the interactions of multiple elements can be predicted based on the measured data with respect to the interactions. The proposed method can form a basis of a generalized prediction method for flow-generated noise produced by multiple elements. The application of the proposed method is supported by two engineering examples.

Dynamic causal models of neural system dynamics: current state and future extensions

Complex processes resulting from interaction of multiple elements can rarely be understood by analytical scientific approaches alone; additional, mathematical models of system dynamics are required. This insight, which disciplines like physics have embraced for a long time already, is gradually gaining importance in the study of cognitive processes by functional neuroimaging. In this field, causal mechanisms in neural systems are described in terms of effective connectivity. Recently, dynamic causal modelling (DCM) was introduced as a generic method to estimate effective connectivity from neuroimaging data in a Bayesian fashion. One of the key advantages of DCM over previous methods is that it distinguishes between neural state equations and modality-specific forward models that translate neural activity into a measured signal. Another strength is its natural relation to Bayesian model selection (BMS) procedures. In this article, we review the conceptual and mathematical basis of DCM and its implementation for functional magnetic resonance imaging data and event-related potentials. After introducing the application of BMS in the context of DCM, we conclude with an outlook to future extensions of DCM. These extensions are guided by the long-term goal of using dynamic system models for pharmacological and clinical applications, particularly with regard to synaptic plasticity.

A prediction method for aerodynamic sound produced by multiple elements in air ducts

A prediction method for aerodynamic sound produced by the interaction of multiple elements in a low speed flow duct has been developed. Same as the previous works of Mak and Yang for two in-duct elements, the concept of partially coherent sound fields is adopted to formulate the sound powers produced by interaction of multiple in-duct elements at frequencies below and above the cut-on frequency of the lowest transverse duct mode. An interaction factor is finally defined as a result of a simple relationship between the sound power due to the interaction of multiple in-duct elements and that due to a single in-duct element. The present study suggests that it is possible to predict the level and spectral distribution of the additional acoustic energy produced by the interaction of multiple in-duct elements. The proposed method therefore can form a basis of a generalized prediction method for aerodynamic sound produced by multiple in-duct elements in a ventilation system.