Oscillation Criteria of Even Order Delay Dynamic Equations with Nonlinearities Given by Riemann-Stieltjes Integrals

We study the oscillatory properties of the following even order delay dynamic equations with nonlinearities given by Riemann-Stieltjes integrals: (p(t)xΔn-1(t)α-1xΔn-1(t))Δ+f(t,x(δ(t))) + ∫aσ(b)k(t,s)x(g(t,s))θ(s)sgn(x(g(t,s)))Δξ(s)=0, where t∈[t0,∞):=[t0,∞)∩, a time scale which is unbounded above, n⩾2 is even, f(t,u)⩾q(t)uα, α>0 is a constant, and θ:[a,b]1→ℝ is a strictly increasing right-dense continuous function; p,q:[t0,∞)→ℝ, k:[t0,∞)×[a,b]1→ℝ, δ:[t0,∞)→[t0,∞), and g:[t0,∞)×[a,b]1→[t0,∞) are right-dense continuous functions; ξ:[a,b]1→ℝ is strictly increasing. Our results extend and supplement some known results in the literature

On Interval-Valued Hesitant Fuzzy Soft Sets

By combining the interval-valued hesitant fuzzy set and soft set models, the purpose of this paper is to introduce the concept of interval-valued hesitant fuzzy soft sets. Further, some operations on the interval-valued hesitant fuzzy soft sets are investigated, such as complement, “AND,” “OR,” ring sum, and ring product operations. Then, by means of reduct interval-valued fuzzy soft sets and level hesitant fuzzy soft sets, we present an adjustable approach to interval-valued hesitant fuzzy soft sets based on decision making and some numerical examples are provided to illustrate the developed approach. Finally, the weighted interval-valued hesitant fuzzy soft set is also introduced and its application in decision making problem is shown

Enhancing thermoelectric properties of isotope graphene nanoribbons via machine learning guided manipulation of disordered antidots and interfaces

Structural manipulation at the nanoscale breaks the intrinsic correlations among different energy carrier transport properties, achieving high thermoelectric performance. However, the coupled multifunctional (phonon and electron) transport in the design of nanomaterials makes the optimization of thermoelectric properties challenging. Machine learning brings convenience to the design of nanostructures with large degree of freedom. Herein, we conducted comprehensive thermoelectric optimization of isotopic armchair graphene nanoribbons (AGNRs) with antidots and interfaces by combining Green's function approach with machine learning algorithms. The optimal AGNR with ZT of 0.894 by manipulating antidots was obtained at the interfaces of the aperiodic isotope superlattices, which is 5.69 times larger than that of the pristine structure. The proposed optimal structure via machine learning provides physical insights that the carbon-13 atoms tend to form a continuous interface barrier perpendicular to the carrier transport direction to suppress the propagation of phonons through isotope AGNRs. The antidot effect is more effective than isotope substitution in improving the thermoelectric properties of AGNRs. The proposed approach coupling energy carrier transport property analysis with machine learning algorithms offers highly efficient guidance on enhancing the thermoelectric properties of low-dimensional nanomaterials, as well as to explore and gain non-intuitive physical insights

A data-driven model to quantify the impact of river discharge on tide-river dynamics in the Yangtze River estuary

Understanding the role of river discharge on tide-river dynamics is of essential importance for sustainable water management (flood control, salt intrusion, and navigation) in estuarine environments. It is well known that river discharge impacts fundamental tide-river dynamics, especially in terms of subtidal (residual water levels) and tidal properties (amplitudes and phases for different tidal constituents). However, the quantification of the impact of river discharge on tide-river dynamics is challenging due to the complex interactions of barotropic tides with channel geometry, bottom friction, and river discharge. In this study, we propose a data-driven model to quantify the impact of river discharge on tide-river dynamics, using water level time series data collected through long-term observations along an estuary with substantial variations in river discharge. The proposed model has a physically-based structure representing the tide-river interaction, and can be used to predict water level using river discharge as the sole predictor. The satisfactory correspondence of the model outputs with measurements at six gauging stations along the Yangtze River estuary suggest that the proposed model can serve as a powerful instrument to quantify the impacts of river discharge on tide-river dynamics (including time-varying tidal properties and tidal distortion), and separate the contribution made by riverine and tidal forcing on water level. The proposed approach is very efficient and can be applied to other estuaries showing considerable impacts of river discharge on tide-river dynamics.info:eu-repo/semantics/publishedVersio

Phylogenetic Relationships in the Festuca-Lolium Complex (Loliinae; Poaceae): New Insights from Chloroplast Sequences

The species within the Lolium/Festuca grass complex have dispersed and colonized large areas of temperate global grasslands both naturally and by human intervention. The species within this grass complex represent some of the most important grass species both for amenity and agricultural use worldwide. There has been renewed interest by grass breeders in producing hybrid combinations between these species and several countries now market Festulolium varieties as a combination of genes from both genera. The two genera have been differentiated by their inflorescence structure, but controversy has surrounded the taxonomic classification of the Lolium-Festuca complex species for several decades. In order to better understand the complexities within the Lolium/Festuca complex and their genetic background, the phylogeny of important examplers from the Lolium-Festuca complex were reconstructed. In total 40 taxa representing the Festuca and Lolium species with Vulpia myuros and Brachypodium distachyon as outgroups were sampled, using two noncoding intergenic spacers (trnQ-rps16, trnH-psbA) and one coding gene (rbcL). Maximum parsimony (MP), Bayesian inference (BI) analyses based on each partition and combined plastid DNA dataset, and median-jointing network analysis were employed. The outcomes strongly suggested that the subgen. Schedonorus has a close relationship to Lolium, and it is also proposed to move the sect. Leucopoa from subgen. Leucopoa to Subgen. Schedonorus and to separate sect. Breviaristatae from the subgen. Leucopoa. We found that F. californica could be a lineage of hybrid origin because of its intermediate placement between the broad-leaved and fine-leaved clade

A Stacked GRU-RNN-based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation

Predictions of renewable energy (RE) generation and electricity load are critical to smart grid operation. However, the prediction task remains challenging due to the intermittent and chaotic character of RE sources, and the diverse user behavior and power consumers. This paper presents a novel method for the prediction of RE generation and electricity load using improved stacked gated recurrent unit-recurrent neural network (GRU-RNN) for both uni-variate and multi-variate scenarios. First, multiple sensitive monitoring parameters or historical electricity consumption data are selected according to the correlation analysis to form the input data. Second, a stacked GRU-RNN using a simplified GRU is constructed with improved training algorithm based on AdaGrad and adjustable momentum. The modified GRU-RNN structure and improved training method enhance training efficiency and robustness. Third, the stacked GRU-RNN is used to establish an accurate mapping between the selected variables and RE generation or electricity load due to its self-feedback connections and improved training mechanism. The proposed method is verified by using two experiments: prediction of wind power generation using multiple weather parameters and prediction of electricity load with historical energy consumption data. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods of machine learning or deep learning in achieving an accurate energy prediction for effective smart grid operation